Computer Science and Informatics (PhD)

2021-22 (also available for 2022-23)

This course is eligible for Doctoral loan funding. Find out more.

Start date

1 October 2021

17 January 2022

25 April 2022

Duration

The maximum duration for a full-time PhD is 3 years (36 months) or part-time is 6 years (72 months) with an optional submission pending (writing up period) of 12 months.

Sometimes it may be possible to mix periods of both full-time and part-time study.

If studying on a part-time basis, you must establish close links with the University and spend normally not less than an average of 10 working days per year in the university, excluding participation in activities associated with enrolment, re-registration and progression monitoring. You are also expected to dedicate 17.5 hours per week to the research.

Application deadlines

For PGR start date September 2021

02 July 2021

About the research degree

A PhD is the highest academic award for which a student can be registered. This programme allows you to explore and pursue a research project built around a substantial piece of work, which has to show evidence of original contribution to knowledge.

A full-time PhD is a programme of research culminating in the production of a large-scale piece of written work in the form of a research thesis that should not normally exceed 80,000 words (excluding ancillary data).

Completing a PhD can give you a great sense of personal achievement and help you develop a high level of transferable skills which will be useful in your subsequent career, as well as contributing to the development of knowledge in your chosen field.

You are expected to work to an approved programme of work including appropriate programmes of postgraduate study (which may be drawn from parts of existing postgraduate courses, final year degree programmes, conferences, seminars, masterclasses, guided reading or a combination of study methods).

You will be appointed a main supervisor who will normally be part of a supervisory team, comprising up to three members to advise and support you on your project.

Entry requirements

The normal level of attainment required for entry is:

  • A Master’s degree or an honours degree (2:1 or above) or equivalent, in a discipline appropriate to the proposed programme to be followed, or appropriate research or professional experience at postgraduate level, which has resulted in published work, written reports or other appropriate evidence of accomplishment.

If your first language is not English, you will need to meet the minimum requirements of an English Language qualification. The minimum for IELTS is 6.0 overall with no element lower than 5.5, or equivalent will be considered acceptable. Read more about the University’s entry requirements for students outside of the UK on our Where are you from information pages.

Why choose Huddersfield?


There are many reasons to choose the University of Huddersfield and here are just five of them:

  1. We were named University of the Year by Times Higher Education in 2013.
  2. Huddersfield is the only University where 100% of permanent teaching staff are Fellows of the Higher Education Authority.
  3. Our courses have been accredited by 41 professional bodies.
  4. 94.6% of our postgraduate students go on to work and/or further study within six months of graduating.
  5. We have world-leading applied research groups in Biomedical Sciences, Engineering and Physical Sciences, Social Sciences and Arts and Humanities.

What can I research?

There are several research topics available for this degree. See below examples of research areas including an outline of the topics, the supervisor, funding information and eligibility criteria:

Outline

The planning performance of state-of-the-art domain-independent planners can be improved by deriving and exploiting knowledge about the domain structure that is not explicitly given in the input formalisation. The type of knowledge to extract and the way for exploiting it, are very interesting topics for the Artificial Intelligence community. Three main knowledge extraction and exploitation approaches have been investigated: reformulation, configuration and combination. Reformulation techniques focus on changing the way the model is described; configuration approaches concentrate on adapting the planner to the specific problem, while combination methods improve overall planning performance by combining different planning systems. This project will investigate innovative techniques for the extraction and exploitation of knowledge in AI planners, in order to improve either the runtime –i.e., the time required for solving a problem– or the quality of generated plans. Specifically, the focus will be on the investigation of mixed reformulation-configuration techniques; some preliminary results (published at IJCAI 2015) confirm the area is promising and worthy investigating.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

How to apply

Outline

Crime scene reconstruction is a forensic science discipline in which one gains explicit knowledge of the series of events that surround the commission of a crime using deductive and inductive reasoning physical evidence scientific methods and their interrelationships. This programme aims at investigating innovative forensic imaging techniques for producing accurate reproduction of a crime scene or an accident scene for the benefit of a court or to aid in an investigation. The programme will start from reviewing the state-of-the-art of 3D imaging techniques such as Augmented Reality and stereoscopy for creating or enhancing the illusion of depth in an image. The research will then propose innovative 3D imaging approaches based on photogrammetry theories and recent developments in remote sensing technologies for the acquisition and understanding of accurate and reliable measurements of a diverse range of natural and manmade structures including underground disturbances. The research encompasses scientific disciplines including image networks and sequences vision metrology laser scanning and range imaging as well as 3D modelling and interactive visualisation. The research output is anticipated to benefit forensic applications such as stockpile monitoring and underground abnormality detection.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

How to apply

Outline

The ability to accurately measure the movement of joints during gait cycles and physical activities is of a particular interest to both patients and clinicians. The quantification of relatively motions between bone anatomies within a joint space is routinely used in the diagnosis of functional disorders in the musculoskeletal system. Common methods for acquiring kinematic measurements depend on a combination of in vivo imaging and motion data acquisition techniques. A separate and often time consuming post-processing is required to recreate motions in a chosen joint space for clinical interpretation.

This proposed project aims to address the bottleneck in the data fusion stage of a kinematic measurement system. Our indicative research domains include: 1) a real-time delineation algorithm for extracting bone surfaces from B-mode ultrasound images; 2) a hardware-accelerated approach to the fusion of multimodal data; 3) presentation of motion analysis for clinical interpretation.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

How to apply

Outline

The purpose of this research project is to develop a web-based Visual Analytics Platform that can be used for the construction of high performance, good quality buildings. The project will focus on the challenges faced by different stakeholders, namely, Government, Energy Providers, Housing Agencies and Householders. The proposed platform will be an opportunity for all stakeholders to collaboratively explore and understand the relationships among different layers of data such as climate and environmental data, building information, demographic and behavioural information. This will help to take well-informed decisions to improve the overall indoor quality of the buildings. The project will address the global challenges linked with digital technologies, stakeholders’ collaboration, and cross-disciplinary data integration in smart homes context.

Objectives

• Develop multi-dimensional data integration framework to explore cross-disciplinary data dependencies that have direct/indirect relation with energy optimisation and Indoor Environment Quality (IEQ).

• Investigate how teamwork among various stakeholders can be enhanced by providing comprehensive Virtual Analytical Platform.

• Develop Web-based Visual Analytical Platform to cross-filter and visualise different level of information to improve indoor quality of living in the buildings. This information can be used as an input for advanced Building Information Modelling (BIM).

The Smart House Facility at University of Huddersfield will be used as test bed to provide extremely valuable datasets related to air quality (e.g., air test, air pollution), energy usage (gas and electricity), appliances data (e.g., refrigerator, TV and radiator Usage), humidity level, CO2 emission and temperature. Energy utilisation data for gas and electricity will be collected and analysed in the virtual platform for different appliances. Data will also be collected for different part of the houses to understand energy usage patterns in the house. These datasets, then, will be visually analysed in a virtual collaborative platform. This platform will help to explore the relationship among different variables to improve the overall quality of living in that space and its comfort level (e.g., thermal and visual comfort).

The realisation of UK’s target of “Net zero energy buildings by 2050” could only be possible by prioritising overall quality of living in the buildings and not just by enhancing energy efficiency. Other factors, such as thermal and visual comfort, fabric efficiency, level of insulation, building orientation, and ventilation are also important. Furthermore, exploring this information together with other attributes such as environmental as well as demographic and behavioural information could play vital role to provide more personalised, adaptive and context aware solutions. (More detail in Smart Homes Research Paper [1].)

Furthermore, it is also very important to understand different stakeholders’ requirements to develop a globally acceptable strategic solution for improving Indoor Environment Quality of smart homes. This could be achieved by providing a virtual collaborative space to explore and investigate if-else scenarios together.

The overall architecture of the platform consists of multiple layers; first layer consists of multi-dimensional datasets (data layer). These datasets will be collected from smart house building present at University of Huddersfield. The recorded datasets will then be organised into their related categories (for instance, energy utilisation data, CO2 emission data, temperature, humidity etc.,). The second layer of system architecture constitutes of services layer; this layer will utilise the recorded datasets to explore what kind of services could be provided into the platform that can help different stakeholders to improve overall quality of living in the buildings. A seamless configuration of the services will be ensured at this layer depending on the needs of the stakeholders. Third layer is the user interface that will help stakeholders to visually interact with the datasets to explore if-else scenarios. This layer will also ensure how to provide cross-filtration functionality in the user-interface to find relationships among different data variables. The last layer of the architecture will be the application layer. This layer will help stakeholders to explore the possibility to apply the knowledge in different domains.

In the quest of designing and developing this platform, this project aims to address the following research questions; what are the key interests of relevant stakeholders in designing improved quality smart buildings? How to provide a seamless integration of cross-domain datasets in one virtual platform? What should be the characteristics of visual analytic platform to explore different level of information to investigate if-else scenarios?

An initial investigation study is already in progress where datasets for pilot studies are acquired from Salford Energy House. The proposed project will provide more opportunities of collaboration in future, nationally and internationally.

The Huddersfield Smart House Research Facility is being developed as a collaborative hub for industry, academia and government organisations. It is being developed to accelerate research and development for smart products and services to be used in the building environment with an aim to bring transformational improvements in key performance indicators corresponding to 21st century houses and living conditions. For this purpose, a well instrumented two storey dwelling is being constructed that will provide facilities for a range of novel and innovative investigations to be carried out.

Smart technologies can help us in reducing carbon footprints as well as having positive energy balance through improved energy performance of homes and buildings. We can achieve greater energy efficiency, cut carbon emissions and support more intelligent and flexible management of energy supply and demand. By incorporating use of smart technologies, the health and wellbeing can be significantly improved through better management of internal environments, safety and security. Smart technologies have potential to offer significant improvements in wellbeing of the occupants by allowing control through voice and mobile apps as well as using automation and artificial intelligence to support and predict our changing needs.

HSHRF aims to bring researchers, practitioners, industries and government organisations together to design, develop and implement holistic solutions to current and future societal challenges associated with building environment and its use.

[1] S. Iram et al. Proc. Companion Proceedings of the10th International Conference on Utility and Cloud Computing (Austin, TX USA 2017)

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

How to apply

Outline

Free structured, semi-structured or unstructured context are very popular in a wide range of applications. Meanwhile, Graph algorithms enhancements to artificial intelligence and Deep Learning (DL) are succeeding on abstracting data complexity and increasing expressiveness. In this research, a contextual graph is proposed for the Sentiment Analysis (SA) that is constructed jointly with a neural network based on graph modelling. A new method will be developed in the subject area to deal with complicated data structures and case situations.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

How to apply

Outline

Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services. By 2020, 50 billions of IoT heterogeneous devices are connected to the internet. These connected IoT devices form an intelligent system of systems that transfer the data without human-to-computer or human-to-human interaction.

While enjoying the convenience and efficiency that IoT brings to us, threats to the IoT devices and applications are on the rise; however patterns within recorded data can be analysed to help predict threats. There are different types of attacks and threats that may diffuse the IoT architecture, such as spoofing, DDoS attacks, unauthorized access, man-in-the-middle attacks and ad hoc networks. One feasible countermeasure against targeted attacks is to apply Intrusion Detection System (IDS) on the IoT network to detect and report intrusion, policy violations, and unauthorized use. Hence a an efficient approach for detecting and predicting IoT attacks is needed.

This PhD project will develop an innovative IDS model for the application and distribution of deep Learning algorithms within IoT networks. There will be a need to develop proof-of-concept simulations for the purposes of benchmarking and evaluation. Applicants should be comfortable with programming in a high level programming or simulation language (C, Python, R, etc.). A background in mathematics/statistics will be useful, as will experience of planning and workflow scheduling systems.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

How to apply

Outline

The aim of this project is to use natural language processing and information retrieval techniques to extract relevant information from medical notes. This will entail developing a thorough understanding of existing methods and tools, testing them on concrete collections of medical notes (e.g. in mental health) and developing novel methods improving on the state of the art. In some cases, medical texts will be semi-structured, thus making analysis easier, but in other cases text will be free, which poses the biggest challenge. Analysis work may have to be carried out in collaboration with medical experts who will assess the validity and usefulness of the extracted knowledge.

The successful candidate will have thorough computer science education, and will have some specialised knowledge in artificial intelligence, natural language processing or information retrieval.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

How to apply

Outline

The Industrial Internet of Things (IIoT) is a challenging technology for businesses. The ability to secure and trust IIoT devices is of paramount importance, and can often be a vulnerability to expose valuable Intellectual Property (IP). There is a need to develop new mechanisms of trust that also provide immutable provenance records. This PhD project will identify security risks in IIoT networks and focus on developing new, smarter, and more agile approaches to ensuing trust in IIoT networks. There will be a need to develop proof-of-concept simulations for the purposes of benchmarking and evaluation, as well as a hardware-in-the-loop demonstrator. Applicants should be comfortable with programming in a high level programming or simulation language (C, Python, R, etc.). A background in mathematics/statistics and network security will be useful, as will experience of interfacing embedded systems and sensors.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

How to apply

Outline

Requirements engineering (RE) is the process of defining, documenting and maintaining requirements in the design process and is considered the key success factor in software systems projects. While requirements elicitation is a relatively mature area of RE, an inability to frame problems and explicitly relate them to approaches is recognised as one of the most glaring deficiencies of practice and theory (Jackson, 1995). Despite this recognition, only a small number of studies have attempted to understand the factors that influence requirements practitioners in their selection of appropriate approaches(s). Given the plethora of available approaches, understanding the factors that influence the selection of requirements engineering (RE) approaches during a software or systems development project remains an open challenge. We use the term approach to mean any technique, tool or method that may be used during RE endeavours. The primary aim of this research is to organise the RE approaches into an ontological framework, based on a series of collaborative studies, that gathers the collective experience and knowledge of RE practitioners and researchers in the selection of complementary RE approaches.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

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Outline

Air pollution became one of the major ecological problems in modern cities. Since bad air increases the health risk, it is very important to study the key factors of air pollution and try to reduce the negative impact. A few studies have shown, that it can be efficiently done by combining data mining techniques and machine learning/AI algorithms.

The project will be done in collaboration with the PARK research centre's road traffic management team, and its industrial and local authority partners. The student will work with a measured air pollution dataset, a traffic flow dataset, and a weather dataset as well as geographical/urban features of the area. The first three datasets are longitudinal and present historical records of measurements. The student will apply data mining techniques to study, summarise the data and identify the combination of factors associated with bad air pollution and changes in air pollution. The student will be working on a predictive model to mine for patterns that lead to the negative/positive prognosis in air pollution.

The ideal candidate will have a strong background in Data Analytics (or/and Machine Learning or/and AI). He would have a solid background in applied mathematics and have experience in R (or/and Python or/ and Matlab) programming.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

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Outline

Data analytics and the use of Artificial Intelligence techniques to forecast outcomes is an area of considerable interest to the Business Intelligence community.

As new business models and technologies such as cloud computing mature, the emergent use of connected devices such as smart phones and Internet of Things (IoT) appliances is creating a demand for resilient Business Intelligence systems that can provide robust data analytics services across different security realms, often in multi-cloud settings.

This PhD project will develop a cross-realm BI model for distributed data analytics services. There will be a need to develop proof-of-concept simulations for the purposes of benchmarking and evaluation, as well as a hardware-in-the-loop demonstrator. Applicants should be comfortable with programming in a high level programming or simulation language (C, Python, R, etc.). A background in mathematics/statistics and network security will be useful, as will experience of signal processing and/or time series data analysis.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

How to apply

Outline

The Internet of Things (IoT) is aimed at connecting billions of things and enable communication and information exchanging among things, where many different types of large directed networks will arise. In large directed networks, there may exist dead-zones, where only incoming edges are available but without outgoing edges from the zone. There may also exist nearly-dead-zones, where the number of outgoing edges is significantly smaller than the number of incoming edges for the whole zone. Finding all such dead-zones and/or nearly-dead-zones in large directed networks is important to analyse and maintain the networks. Large (nearly-)dead-zones may also contain small (nearly-)dead-zones. For example, a given standalone directed network as a whole, is a dead-zone. This means that we can build up a hierarchical structure of (nearly-)dead-zones for any given large directed networks. Your tasks in this project mainly include: (1) Identify research gaps between existing approaches for finding dead-zones and our target - dead-zone finder in large directed networks; (2) Design efficient algorithms to find (nearly-)dead-zones in large directed networks; and (3) Evaluate your proposed algorithms against existing algorithms. The application of the research results from this project will help us to understand the community structures and communication patterns across large directed networks in IoT.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

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Outline

Fog computing encourages a shift of handling everything in the core of cloud computing to handling at the edges of a network. To be specific, instead of sending all the data collected to the cloud, fog computing suggests to process the data at leaf nodes or at the edges. This idea is also called ‘edge analytics’. In smart healthcare at home, sensing data about inhabitants or patients should be analysed and processed in a near-real-time manner. To enable this, fog computing technologies can be used to distribute the healthcare data analytics onto fog computing nodes and communication with remote cloud servers only takes place when necessary. In such scenarios, it will be critical to process smart healthcare sensing data at home efficiently and effectively on fog nodes. To this end, this project aims to develop novel home healthcare and remote monitoring solutions. Your main tasks in this project include: (1) Study some smart home simulation tools, such as OpenSHS; (2) Incorporate smart healthcare into the simulation tools; (3) Develop novel approaches based on fog computing paradigms to achieve near-real-time processing of healthcare sensing data.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

How to apply

Outline

Research in Artificial Intelligence (AI) has generally followed two directions: symbolic approaches, e.g. based on knowledge representation and reasoning, and sub-symbolic/statistical approaches, such as the various machine learning algorithms. One notable difference is that while symbolic approaches are easily explainable, many statistical approaches are less interpretable, if at all. This is an important barrier to the adoption of machine learning in areas where explanations are required, such as healthcare or business applications. The purpose of this PhD research is three-fold: - investigate the interpretability of the most well-known representatives of various machine learning approaches, such as decision trees, neural networks and Bayesian models; - explore model-specific or model-agnostic methods that have been proposed in literature; - propose new methodologies that enhance interpretability, either providing interpretability of black box models, or increasing interpretability of grey or white models, possibly combining machine learning models with features of symbolic AI, such as knowledge representation and reasoning.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

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Outline

The emergence of big data has stimulated interests in domain specific knowledge extraction. In turn, this can motivate novel approaches to the formulation of strategic decisions and planning. With an ever-increasing decentralised approach to data acquisition, one key challenges lies in the modelling of data that meets the domain requirements of a specific application. For a data analytics pipeline to be domain-specific and explainable, a data model needs to contain semantic descriptors and logical structures that are intrinsic to the domain of interest. To achieve this, the feature space underlying a data model should be tightly coupled with the ontology of a specific knowledge domain, e.g. education, physics and chemistry.

This proposed PhD will focus on developing the theoretical design and implementation of automatically deriving ontologically based features spaces from open data sets. The successful applicant will need to have experience of applied statistics, forecasting and model generation, along with some familiarity of high-level programming languages (such as C, C#, and Python) and software tools (R, Tableau, KNIME).

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

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Outline

Mental disorders and diseases affect a large proportion of the whole world population, including children, adolescents and elderly people, causing disabling and life-threatening conditions. They are cause for inequity, social stigma and discrimination, and human rights violations. Care services are limited, costly and often insufficient. In this context non-pharmacological interventions are increasingly important for health and social care. This project offers to students the opportunity to investigate uses of technology to enhance the design, implementation and evaluation of non-pharmacological interventions through addressing one of the following research lines: a) delaying onset and progression of disorders and diseases (e.g. through personalized and adaptive training of key psycho-physical capabilities); b) preserving general function and autonomy (e.g. through augmentation of cognitive, affective and behavioural capabilities, and technology-mediated, personalized activity monitoring and support); c) promoting engagement in daily life activities (e.g. through technology-mediated, personalised activity monitoring, adaptive support and stimulation of activity); d) enhancing rehabilitation (e.g. through personalized and adaptive monitoring and support of rehabilitation activities); e) enhancing social integration (e.g. through social networking technologies to facilitate meaningful social interactions remotely); f) enhancing the personalization, quality and efficiency of care provided by formal and informal caregivers (e.g. through facilitating the monitoring of sufferers’ conditions and activity, the provision of adaptive and personalized activity support, and remote interactions between sufferers and caregivers). To address these lines, students will investigate innovative approaches integrating complexity science, systemic design, human factors and ergonomics, and exploring novel uses of cutting-edge technologies including: assistive technologies; Internet of Things; autonomous agents; affective computing; deep learning; context-aware computing; biometric monitoring; activity tracking; augmented and virtual reality; digital games; social media. Projects will focus on high social impact mental disorders and diseases such as dementia, autism, attention deficit hyperactivity and brain injury.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

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Supervisors

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Outline

The proposed research work aims to investigate the impact of different factors on energy consumption patterns in residential as well as in commercial buildings. An automated web-based intelligent visual analytics platform, dashboards and other visual artifacts will be designed and developed to understand the complex data sets and to explore their relationships with each other. This platform will help to filter information at different level of complexity for different stakeholders such as house holders, housing agencies, energy providers, and policy makers to improve their decision making processes.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

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Outline

In terms of scale the Boston Consulting Group states that “IoT sensors and devices are expected to exceed mobile devices as the largest category of connect devices in 2018, growing at 23%”. Key markets that will benefit from this technology push are advanced robotics (digital manufacturing), connected and autonomous vehicles, and the emerging health and wellness monitoring market.

These large-scale networks present opportunities and challenges to learn from massive, distributed datasets. Algorithms to perform supervised and unsupervised learning can enable insight to be derived from data. Such data is collected and often analysed locally by individual IoT devices, presenting difficulties when governing the distribution of learning algorithms to arrive at robust conclusions for queries.

This PhD project will develop a governance model for the application and distribution of Machine Learning algorithms within IoT networks. There will be a need to develop proof-of-concept simulations for the purposes of benchmarking and evaluation. Applicants should be comfortable with programming in a high level programming or simulation language (C, Python, R, etc.). A background in mathematics/statistics will be useful, as will experience of planning and workflow scheduling systems.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

Supervisors

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Outline

In the Internet of Things (IoT), security is a critical area, which should be carefully studied when promoting the adoption of IoT technologies in real-world applications. IoT security is concerned with safeguarding connected devices and networks and restricting unauthorised access to private data and devices. It is widely said that much of the increase in IoT communication comes from computing devices and embedded sensor systems used in industrial machine-to-machine (M2M) communication, smart energy grids, home and building automation, vehicle to vehicle communication and wearable computing devices. In such scenarios, due to the mobility and failure-prone connectivity issues, there is much dynamics to be addressed in order to improve security. In this project, we model the IoT security solutions using graph-based techniques, where data access among IoT devices is represented as graphs. Your main tasks in this project include: (1) Construct data access graphs for IoT devices using simulation tools like NetSim, OMNeT++, etc.; (2) Evaluate the security safeguarding performance based on the constructed data access graphs by incorporating dynamics into these graphs; (3) Design and recommend new designs for data access among IoT devices so as to improve security for all devices.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

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Supervisors

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Outline

Large-scale HPC cluster systems are finding increasing deployment in academic, research, and commercial settings, hence securing the HPC infrastructure is an important task. There are many security threats coming from both the Internet and internal networks. It is crucial to introduce an adequate level of security to the infrastructure, to prevent an unauthorised access to the HPC resources and avoid loss of valuable data. The aim of this project is to investigate the security issues in HPC systems, and devise a framework suitable for securing HPC cluster systems in Higher Education and research institutions. The challenge is to secure internal distributed resources against unauthorised access while permitting easy access by legitimate users, to coordinate security across different node platforms and different specialised function nodes (there is a separation of nodes into 'head nodes', 'compute nodes', 'storage nodes', and 'management nodes'), and to maintain the integrity of all nodes since many nodes share identical configurations. This framework should provide coordination between security domains on campus and between institutions when resources are shared across multiple organisations. It should address the security issues posed by the high-bandwidth connections, extensive computational power, massive storage capacity, and should enable process monitoring, network port scanning and traffic analysis.

Funding

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Deadline

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Outline

Research on recognition of Activities of Daily Living (ADL) in a smart home environment has generally followed two distinct directions. On the one hand, knowledge-rich rule-based systems have been developed that rely on rules of the form IF one or more sensor-based events are detected THEN a particular activity or activities are recognised. These systems are easily explainable but require expertise in encoding the rules. On the other hand, the proliferation of machine learning techniques has led to research that relies on learning activity patterns from large sets of sensor data. These approaches can handle uncertain or faulty sensor data but rely mostly on black-box techniques which are not interpretable.

The increased demands of activity recognition in real-world case studies require hybrid approaches that combine the strengths and mitigate the weaknesses of rule-based and data-driven approaches. Hybridisation can follow one of these directions:

• Handcrafted rules can be used as features in machine learning algorithms.

• Results of rule-based reasoning can be used to narrow down machine learning search space.

• Rules can be learned from data instead of being hand crafted by experts.

• Two-level recognition: machine learning performed directly on raw sensor data, followed by higher-level rule-based reasoning.

The proposed research will advance state of the art in ADL recognition by helping lift restrictions and assumptions imposed in current rule-based and machine learning approaches to account for the peculiarities of each approach. For instance, the rule-based approach in [1] is capable of providing ADL recognition in a smart home in both offline and real-time modes, but has to rely on significant expertise to hand craft the rules and operates under the assumption that there is a single inhabitant in the house. On the other hand, the approach in [2] uses Conditional Random Fields to successfully recognise activities in two-resident datasets but assumes that sensors are independent of each other and its results are not easily interpretable.

A hybrid approach that combines high performance with interpretability, such as a neuro-symbolic one would be suitable in real-world applications such as home healthcare, where there are increased needs for highly accurate, real-time management of health and well-being conditions, while at the same time explanations for any decision are required for any solution to be trustworthy and able to be widely adopted.

The Huddersfield Smart House Research Facility is being developed as a collaborative hub for industry, academia and government organisations. It is being developed to accelerate research and development for smart products and services to be used in the building environment with an aim to bring transformational improvements in key performance indicators corresponding to 21st century houses and living conditions. For this purpose, a well instrumented two storey dwelling is being constructed that will provide facilities for a range of novel and innovative investigations to be carried out.

Smart technologies can help us in reducing carbon footprints as well as having positive energy balance through improved energy performance of homes and buildings. We can achieve greater energy efficiency, cut carbon emissions and support more intelligent and flexible management of energy supply and demand. By incorporating use of smart technologies, the health and wellbeing can be significantly improved through better management of internal environments, safety and security. Smart technologies have potential to offer significant improvements in wellbeing of the occupants by allowing control through voice and mobile apps as well as using automation and artificial intelligence to support and predict our changing needs.

HSHRF aims to bring researchers, practitioners, industries and government organisations together to design, develop and implement holistic solutions to current and future societal challenges associated with building environment and its use.

[1] Baryannis G., Woznowski P., and Antoniou G. Rule-based Real-time ADL Recognition in a Smart Home Environment. 10th International Web Rule Symposium (RuleML) 2016

[2] Twomey, N., Diethe, T., Craddock, I. and Flach, P., 2017. Unsupervised learning of sensor topologies for improving activity recognition in smart environments. Neurocomputing, 234, pp. 93-106

Funding

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The Internet of Things (loT) is an exciting development of a collection of emerging technologies that bring together physical objects to exchange data with each other via wireless and physical network infrastructure. One example of particular interest is the potential of Wireless Sensor Networks (WSN), combined with computational power at the 'edge' of a network (otherwise known as 'Edge Computing' or 'Fog Computing'), to provide data that is analysed both at source and along the network path, to an eventual destination. Such data is produced in massive volume and at high velocity, which is generally beyond the limits of current technologies for storage and processing.

This PhD project would investigate approaches to performing real-time analytics upon data from a range of devices (from simple sensors and embedded systems, right through to complex multi-cloud distributed repositories), to enable valuable insight to be delivered across networks with limited bandwidth. There will be a need to develop proof-of-concept prototypes for the purposes of benchmarking and evaluation.

Applicants should be comfortable with high-level programming, as well as being able to specify and configure distributed file system software and analytics platforms. A background in networking (particularly ad-hoc/opportunistic wireless) will be useful, as will familiarity with data from Field Programmable Gate Array (FPGA) devices.

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Healthcare offers unique challenges for the deployment of machine learning models where the demands for interpretability and performance in general is much higher as compared to most other domains. Given that the cost of model misclassification is potentially high, explanations with respect to how a machine made conclusion is derived play a significant role informing clinicians making unbiased decisions. Knowledge-based systems aim to represent knowledge explicitly via tools such as if-then rules, which allow such a system to reason about how it reaches a conclusion and to provide explanation of its reasoning to end users. Fuzzy systems have been considered effective in building such rule-based systems with one of the most important advantages lying in their inherent interpretability as they support the explicit formulation of, and inference with, domain knowledge, gaining insights into the complex problems and facilitating the explanation of their solutions.

The aim of this PhD project is to develop fuzzy rule-based systems with a particular focus on scenarios of healthcare systems. At the initial phase, the project will look into a number of existing approaches proposed to address the interpretability issues of medical systems, as well as the recently established fuzzy rule-based models. A core part of the project will involve the design and implementation of a specific fuzzy rule-based model that will work with carefully selected healthcare aspects. The implemented system will be evaluated with respect to simulated bench mark data sets first, followed by a close examination of how such a system may perform in collaboration with medical doctors when applied to a diagnostic problem of realistic complexity.

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Here we seek to draw great benefit from the greatly increasing role of Low Earth Orbit satellites, which can certainly be also related to systems in operation in geostationary orbits and elsewhere, perhaps even at Lagrange points, of the Earth and Sun. The latter is where the James Webb Space Telescope will be functioning. For Low Earth Orbit, we wish to avail of the growing use of CubeSat missions. Their safe functioning and performance monitoring can be supported by the mapping of space debris and related mapping, carried out by LeoLabs Ltd. (https://www.leolabs.space). This project aims are further supporting such satellites and their functioning, as well as their role, increasingly also of relevance for environmental disaster relief, for construction planning and development, for agriculture, and national statistical authorities (the latter, for data sourcing).

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The development of domain-independent planners is leading to the use of this technology in a wide range of applications. This is despite the complexity issues inherited in plan generation, which are exacerbated by the separation of planner logic from domain knowledge.

The planning performance of domain-independent engines can be improved by exploiting automatically derived knowledge about the domain or problem structure that is not explicitly given in the input formalisation. The type of knowledge to extract and the way for exploiting it are very interesting topics for the Artificial Intelligence community.

At the state of the art, three main approaches have been investigated: reformulation, configuration and combination. Reformulation techniques focus on changing the way the model is described and provided as input; configuration approaches concentrate on adapting the planner to the specific problem, by changing its internal behaviour. Finally, combination methods improve overall planning performance by combining different planning systems and/or different learning approaches.

This project will investigate innovative techniques for the extraction and exploitation of knowledge in Al planners, in order to improve either the runtime -i.e. the time required for solving a problem - or the quality of generated plans.

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Learning Analytics is an increasingly important area of research which has applications both within education and more broadly within organisations and society. In order to develop effective analytical algorithms it is important to better understand personalised learning. What makes a learning experience “personalised”, in the eye of the learner? To what extent and how can perception of personalisation be ascertained and monitored throughout the learning experience? What can we learn from games about all this? To address these questions, this study will investigate personalisation in learning and learning analytics in relation to this from a game-based perspective. Through a human factors and ergonomics approach, prototypical game systems and learning systems will be analysed and compared. Accordingly, mechanics that define personalisation of learning in both game-based and non-game contexts will be modelled. These mechanics will then be analysed to identify measurable indicators of personalisation of the learning experience. A learning analytics framework will finally be formulated based on the identified indicators, and tested through appropriate mixed methods approaches.

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Modern scientific and engineering research is highly dependent on software. Its importance in driving forward advances in research in the field of computational science and engineering (CSE) has resulted in calls for it to be classified as a first-class, experimental scientific instrument. However, software as a research instrument has not reached a level of maturity compared with the conventional tools of empirical and theoretical science. Research software is principally developed by end-user developers who have a limited understanding and application of fundamental software engineering concepts, principles, and techniques, combined with a "code-first" approach to development, which is in part driven by the perceived complexity and uncertainty of the problem. This results in research software with suboptimal software design, if any, accidental complexity, technical debt, code smells, and an increase in the risk of software entropy. The consequence of this approach is a pathway to stagnation, decay, and the long-term decline of essential research software investment. It has been widely recognised that the future of scientific and engineering enterprise requires a resilient eco-system of software.

The aim of this project is to investigate the development of a software testing framework for measuring the sustainability of scientific and engineering software.

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Current Autonomous Agents with cognitive capabilities based on an internal domain model can perform human like reasoning, and explain their reasoned decision on the basis of their domain model and their situational awareness. Unfortunately, state of the art systems have little or no learning / adaptation capabilities in order to maintain their understanding of the world, and hence maintain their reasoning and explanation capability over time. This project will be aimed at techniques for the knowledge acquisition and maintenance of the domain models, and use the capability of automated planning over a long term horizon as a method to measure progress.

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Gene expression profiles corresponding to specific treatments for cancer patients may hold information about how efficient the treatment is. Comparing genomic profiles of various categories of patients undergoing treatments may help to extract this information. Microarray analysis and meta-analysis are the traditional statistical tools which have proved to be efficient for this purpose. Machine Learning (ML) tools, such as clustering, Principle Component Analysis (PCA), Support Vector Machine (SVM), Random Forest (RF), and most recently Deep Learning methods are also used. It is known that using an ensemble of ML techniques may enhance the accuracy of a model. This approach is also applied in studying genomic profiles. It is suggested that the methods help to select the most important features which contribute to a successful treatment. This research aims to investigate the capacity and accuracy of ML models for identifying prognosis biomarkers and to compare the results with traditional statistical tools. The ultimate aim is to build a high accuracy ML-based model which can help clinicians in choosing the most appropriate and tailored treatment for cancer patients. This will then reduce problems associated with drug resistance and also side effects associated with current cancer therapies.

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Malaria is a severe disease which affects hundreds of millions of people across the world. If not treated in time it can be fatal. According to the World Health Organization (WHO), Africa is especially badly affected and carries 94% of the total malaria deaths, most of them children under the age of 5. According to the most recent studies, WHO suggests that due to COVID-19, malaria cases could be doubled in the oncoming year. This makes investment in malaria research even more important.

Currently, there are three main methods to diagnose malaria, with analysis of microscope images being the most common tool. Microscope images, and fluorescence in particular, can also be used to study how malaria drugs work and help to develop an efficient malaria treatment. The recent advances in Artificial Intelligence (AI) allow us to analyse samples more accurately and faster than a human eye would do. ML methods, and Deep Learning (DL) in particular, are extensively applied to medical images for analysis and classification.

This project is designed to work on the development and testing of ML-based techniques to guide malaria diagnostics and malaria treatment. The project will be done in collaboration with the expert from the London School of Hygiene and Tropical Medicine. It is aimed to use the images obtained during experiments in fluorescence microscopy. A set of ML methods are to be tested on microscope images to help cell segmentation, image analysis and/or classification.

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Machine learning from sensors and other hard data streams is often used for manufacturing applications in academia and is generating great interest in industry. Great additional benefit could be derived from encoding human knowledge in a suitable way for processing in tandem with “hard data”.

This project will use the machine tool maintenance domain, where descriptions of the conditions can be highly subjective, as a focus for methods of converting extracted human knowledge into a usable data format, including a measure of confidence in the translation as part of the algorithm. This will require the candidate to learn about the issues associate with human perception and its impact upon the fuzzy nature of the extracted data.

The candidate should have a strong background in computer science and a desire to understand the links with human factors and applied cognitive psychology. The project will be supported by experts from the psychology and engineering departments. .

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This project will look at developing mathematical models of brewing coffee with the aim of understanding the recent surprising result that in espresso brewing there is a peak in extraction considered as a function of grind size [1]. This model will look at extending existing models of coffee brewing [1,2] to include inhomogeneous and multiphase flow. Methods used will include analytic techniques such as nondimensionalisation and asymptotic reduction and numerical techniques such as finite difference approximations implemented in, for example, matlab.

[1] Cameron, M.I., Morisco, D., Hofstetter, D., Uman, E., Wilkinson, J., Kennedy, Z.C., Fontenot, S.A., Lee, W.T., Hendon, C.H. and Foster, J.M., 2020. Systematically improving espresso: insights from mathematical modeling and experiment. Matter. [2] Moroney, K.M., Lee, W.T., Brien, S.O., Suijver, F. and Marra, J., 2016. Asymptotic analysis of the dominant mechanisms in the coffee extraction process. SIAM Journal on Applied Mathematics, 76(6), pp.2196-2217.

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This project will focus on understanding a specific example of two phase flow instability: the waves of sinking bubbles seen in a settling pint of stout beer. The partial differential equations of dispersed two phase flows will be used to model this system. These equations will be simplified and reduced by nondimensionalisation and asymptotic analysis. Linear stability analysis will be used to model the onset and nature of the instability: this step will probably require numerical methods such as finite difference methods implemented, for instance, in matlab.

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Modern scientific and engineering research is highly dependent on software. Its importance in driving forward advances in research in the field of computational science and engineering (CSE) has resulted in calls for it to be classified as a firstclass, experimental scientific instrument. However, software as a research instrument has not reached a level of maturity compared with the conventional tools of empirical and theoretical science. Research software is principally developed by end-user developers who have a limited understanding and application of fundamental software engineering concepts, principles, and techniques, combined with a "code-first" approach to development, which is in part driven by the perceived complexity and uncertainty of the problem. This results in research software with suboptimal software design, if any, accidental complexity, technical debt, code smells, and an increase in the risk of software entropy. The consequence of this approach is a pathway to stagnation, decay, and the long-term decline of essential research software investment. It has been widely recognised that the future of scientific and engineering enterprise requires a resilient eco-system of software. Research Software Engineering (RSE) aims to facilitate the creation of well-designed, reliable, efficient software to solve research problems. However, there is little empirical evidence to demonstrate that research software is well designed, if at all, understandable, maintainable and extensible.

The aim of this project is to investigate the sustainability of essential research software investment.

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Microservices are a new architectural style in which distributed applications are broken up into small independently deployable services, each running in its own process and communicating via lightweight mechanisms. One of the key aspects of a microservices application is that its complexity is pushed from the components (i.e., services) to the integration (i.e., architectural) level. The challenge of getting insights regarding the performance relationship between such services remains open. The aim of this research is to investigate new approaches for detecting and measuring performance degradation events in a microservice architectures.

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Traditional monolithic architectures no longer meet the needs of scalability and rapid development life cycles. Microservices present a new architectural style in which distributed applications are broken up into small independently deployable services, each running in its own process and communicating via lightweight mechanisms. However, the migration process is not trivial. Migrating monolithic software systems into microservices requires the application of decomposition techniques to find and select appropriate service boundaries. These techniques are often based on domain knowledge, static code analysis, and non-functional requirements such as maintainability. This research aims to develop new migration and decomposition techniques to detangle a monolithic system in order to migrate it to a microservice architecture.

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Recognition of Activities of Daily Living (ADL) and indoor localisation in a smart home environment have attracted considerable interest within the wider research field of Ambient Intelligence. One research strand addresses these tasks through knowledge-rich rule-based systems that rely on rules of the form IF one or more sensor-based events are detected THEN a particular activity or activities are recognised or an inhabitant is located. The complexity of expressing such rules in a multi-modal sensor setup often requires the assumption that, at any given time, there is only one individual within the house. This precludes their applicability in multi-inhabitant scenarios, such as house sharing, families or live-in carers. To remove this assumption, a number of open issues need to be addressed, such as:

• Indoor localisation needs to be more fine-grained than room-level and disassociated with activity recognition (a person detected in a room may not be the person carrying out the recognised activity).

• Person (re-)identification based on video or infrared sensors needs to take into account simultaneous presence of more than one individual.

• Increased need for conflict detection and resolution in the recognition and localisation processes.

The proposed research will advance state of the art in rule-based ADL recognition and indoor localisation by increasing applicability of such approaches. One major area where existing rule-based approaches [1] (even enhanced with conflict detection and resolution [2]) would not be enough is healthcare scenarios, such as home care, live-in care or assisted living. In such cases, smart home technologies are leveraged to aid early diagnosis, lifestyle change and the ability of patients to live at home. This is becoming increasingly important as the UK, like most nations around the world are faced with rapidly increasing numbers of patients with long term health conditions. In many cases, these technologies will only be successful if they can to differentiate between different patients (e.g. a couple living in the same house) or between a patient and a carer.

The advantage of a rule-based approach to address multi-inhabitant activity recognition and localisation, as opposed to ones based on machine learning, is the natural form and explainability of rules, which promote acceptability and trustworthiness, two important requirements in healthcare-related and other settings.

The Huddersfield Smart House Research Facility is being developed as a collaborative hub for industry, academia and government organisations. It is being developed to accelerate research and development for smart products and services to be used in the building environment with an aim to bring transformational improvements in key performance indicators corresponding to 21st century houses and living conditions. For this purpose, a well instrumented two storey dwelling is being constructed that will provide facilities for a range of novel and innovative investigations to be carried out.

Smart technologies can help us in reducing carbon footprints as well as having positive energy balance through improved energy performance of homes and buildings. We can achieve greater energy efficiency, cut carbon emissions and support more intelligent and flexible management of energy supply and demand. By incorporating use of smart technologies, the health and wellbeing can be significantly improved through better management of internal environments, safety and security. Smart technologies have potential to offer significant improvements in wellbeing of the occupants by allowing control through voice and mobile apps as well as using automation and artificial intelligence to support and predict our changing needs.

HSHRF aims to bring researchers, practitioners, industries and government organisations together to design, develop and implement holistic solutions to current and future societal challenges associated with building environment and its use.

[1] Baryannis G., Woznowski P., and Antoniou G. Rule-based Real-time ADL Recognition in a Smart Home Environment. 10th International Web Rule Symposium (RuleML) 2016

[2] Filippaki, C., Antoniou, G., & Tsamardinos, I. (2011). Using constraint optimization for conflict resolution and detail control in activity recognition. Proceedings of the Second international conference on Ambient Intelligence (AmI'11), 41–60

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Interest in the Internet of Things (IoT) is increasing the demand for new design approaches that can assist the specification of resilient, distributed architectures. A market analysis report by Boston Consulting Group (BCG) “Winning in IoT: It’s All About the Business Processes, https://www.bcg.com/perspectives/218353” predicts that $267 billion will be spent on IoT technologies by 2020. IoT devices already produce massive volumes of data, which places significant demands upon network infrastructure. Mobile devices, that move in, out and between networks, place extraordinary demands upon network management services, which need to keep track of individual device identifiers, as well as knowing which devices to trust.

This PhD project will investigate Multiagent Systems approaches to the design, modelling and evaluation of IoT architectures, exploring the use of goal directed behaviour abstractions to model wired and wireless IoT nodes as part of a larger network. There will be a need to develop proof-of-concept simulations for the purposes of benchmarking and evaluation.

Applicants should be comfortable with programming in a high level programming or simulation language (C, Python, R, etc.). A background in mathematics/statistics will be useful. Experience of predicate/modal logic would be beneficial though not essential.

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The Huddersfield Smart House Research Facility is a well-instrumented, 2-storey dwelling that is intended to showcase how technology can make a positive contribution to living spaces encompassing health, the environment, enjoyment and entertainment, and security. A challenge that arises whenever a large number of sensors are integrated is how to best process the high volumes of data that are generated. The processing requirements can be onerous as in order to extract most value from the data sophisticated machine learning and artificial intelligence algorithms will have to be used. One approach would be to use the domestic communications infrastructure to convey all sensor data to a central compute server where the required algorithms run. This approach has drawbacks though in that the conveyance of the data could place a disruptive load on the communications infrastructure to the detriment of other applications such as entertainment. The use of edge processing where data is processed at, or near to, the sensor can enable the transformation of the raw sensor data into knowledge that is then communicated to a central server where decisions are taken that impact the whole house.

Edge devices usually have very limited processing power as they will be based on technology similar to e.g., Arduino microcontrollers as the cost of edge devices has to be low. There is also the possibility of using low-cost edge devices based on FPGAs that could provide quick but inflexible processing. The challenge addressed by this project is how to best partition tasks across an estate of processing devices of varying capabilities (i.e. what the nodes can do) and capacities (i.e. how much of what they do they can do). There are very interesting trade-offs here that involve network traffic – moving data around the house to centralised processing capability and moving capability around in terms of software/firmware updates delivered to edge devices, performance – how quickly a particular task is done, and cost – the complexity of the solution required to deliver the required behaviour.

The project will involve the development of detailed simulations of alternative approaches and the building and characterisation of test-beds located in the smart house. Both of these will contribute to a deeper understanding of optimal approaches to the provision of the required computational capacity and capability to enable smart living in future residential properties. The test-beds will comprise of a number of sensors and associated edge processing devices distributed throughout the smart house, and will be installed very early on in the project so that the practical data gathering and the simulation work will both support each other. The sensors and edge devices are already available within CindA.

The Huddersfield Smart House Research Facility is being developed as a collaborative hub for industry, academia and government organisations. It is being developed to accelerate research and development for smart products and services to be used in the building environment with an aim to bring transformational improvements in key performance indicators corresponding to 21st century houses and living conditions. For this purpose, a well instrumented two storey dwelling is being constructed that will provide facilities for a range of novel and innovative investigations to be carried out.

Smart technologies can help us in reducing carbon footprints as well as having positive energy balance through improved energy performance of homes and buildings. We can achieve greater energy efficiency, cut carbon emissions and support more intelligent and flexible management of energy supply and demand. By incorporating use of smart technologies, the health and wellbeing can be significantly improved through better management of internal environments, safety and security. Smart technologies have potential to offer significant improvements in wellbeing of the occupants by allowing control through voice and mobile apps as well as using automation and artificial intelligence to support and predict our changing needs.

HSHRF aims to bring researchers, practitioners, industries and government organisations together to design, develop and implement holistic solutions to current and future societal challenges associated with building environment and its use.

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The mental health of higher education students has been a prevalent matter with a significant number of students increasingly declaring poor mental wellbeing, which is at greater risk due to the COVID-19 pandemic and associated social distancing measures. The academic performances and social interactions can be significantly impaired as a result of a poor mental state, which itself can manifest through physiological and behavioural changes. There is therefore a strong need for the long-term follow up and assessment of student mood and mental states.

Recent advancements in digital technology and artificial intelligence have facilitated the development of smart products and sensor devices, which can be adopted in a smart indoor environment to promote health and wellbeing through monitoring physical health and managing internal environment. Traditional statistics based approaches may only reflect the mental state trends of general student population, which does not take into account individual characteristics. The employment of wearable devices and ambient sensors in a smart learning environment enables the collection of individual physiology indicators, which can serve as significant features for the development of a personalised predictive model that can track the mental state of individual students.

The project aims to empower students to understand their personal mental state to facilitate self-management and to assist higher education providers to better understand and support each of their students as individuals. This will be achieved by first designing an automatic monitoring system capable of capturing student-centred personal physiology indicators as well as environmental factors, through relevant wearable sensors and ambient devices in a smart indoor learning environment. These then serve as inputs for the design of an adaptive system that can periodically performs personalised mental state recognition and prediction over time through advanced machine learning algorithms.

Supported with the recently established Huddersfield Smart House Research Facility (HSHRF), this makes it possible to monitor regular student activities taken place indoors, e.g., lab experiments research students regularly undertake in HSHRF, where personal physiological indicators such as heart rate, pulse rate and skin temperature, can be measured by wearable sensors like a smart band; and the environmental factors such as room temperature, PM2.5 and humidity, can be measured by air quality monitor. The collection of these data can then be merged together to form an overall data set to be analysed for the extraction of novel features and patterns, followed by the employment of recent machine learning algorithms to identify and predict any changes in physical behaviours and personal physiology that may be indicators of mental state changes.

We are seeking a highly motivated individual with a strong academic background, as demonstrated with a 1st class degree, or equivalent, in a Computer Science or Computer Engineering. The candidate will have demonstrable knowledge in machine learning and possibly experience with mobile and wearable or sensing data. The candidate should also have the proficiency in the use of mainstream data analytics platforms such as Python and Matlab. Good professional writing skills are also expected.

The candidate will be supervised by an interdisciplinary team of experts specialising Artificial Intelligence, Internet of Things and Mental Health Research. The fully-funded studentship will be hosted at the Huddersfield Smart House Research Facility, School of Computing and Engineering, University of Huddersfield.

For informal enquiries please contact Dr Tianhua Chen (https://pure.hud.ac.uk/en/persons/tianhua-chen). Please note that this studentship is open to both UK nationals and international candidates.

The Huddersfield Smart House Research Facility is being developed as a collaborative hub for industry, academia and government organisations. It is being developed to accelerate research and development for smart products and services to be used in the building environment with an aim to bring transformational improvements in key performance indicators corresponding to 21st century houses and living conditions. For this purpose, a well instrumented two storey dwelling is being constructed that will provide facilities for a range of novel and innovative investigations to be carried out.

Smart technologies can help us in reducing carbon footprints as well as having positive energy balance through improved energy performance of homes and buildings. We can achieve greater energy efficiency, cut carbon emissions and support more intelligent and flexible management of energy supply and demand. By incorporating use of smart technologies, the health and wellbeing can be significantly improved through better management of internal environments, safety and security. Smart technologies have potential to offer significant improvements in wellbeing of the occupants by allowing control through voice and mobile apps as well as using automation and artificial intelligence to support and predict our changing needs.

HSHRF aims to bring researchers, practitioners, industries and government organisations together to design, develop and implement holistic solutions to current and future societal challenges associated with building environment and its use.

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Quantum computers leverage the quantum mechanical phenomena of superposition and entanglement to create states that scale exponentially with number of quantum bits. However, the software to be run on a quantum computer is very different from software for classical computers resulting in solutions are hand-crafted in an ad-hoc manner. A solid foundation for building and integrating quantum software is missing. The aim of this research would be to investigate the architectural styles and tactics of quantum software to achieve reliable and robust software architectures for quantum computing.

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Biometric encompasses the measurement and utilities of biological features from human or other mammals. Due to its uniqueness in relation to disease markers and personal identity, biometrics have been applied to medical diagnosis and security. Some prime examples include finger prints, the geometry of salient facial features and irises. In a general sense, biometric applications are underpinned by the fundamentals of image pattern recognition. One of the challenges is the building of a feature set and space that are robust for the purpose of pattern recognition. This problem is particularly aggravated when the saliency of features are unknown, e.g. in iris pigment. In such cases, we ideally need to examine all discernible features from a given source image. This in turn increases the computational requirement and complexity of biometric applications.

This proposed PhD project will investigate methods for enhancing the geometric pattern extraction capability in biometric applications. Depending on the candidate's experience and background, the problem posed here can be explored from two perspectives: 1) a responsive signal filtration pipeline based on the geometric complexity of features; 2) a multi-spectral imaging technique exploiting the non-visible spectra to enhance the acquisition of biometric images.

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In terms of scale the Boston Consulting Group states that “IoT sensors and devices are expected to exceed mobile devices as the largest category of connect devices in 2018, growing at 23%”. Key markets that will benefit from this technology push are advanced robotics (digital manufacturing), connected and autonomous vehicles, and the emerging health and wellness monitoring market.

IoT networks are expected to expand and contract dynamically, and there is an assumption that wireless networking will be an essential part of any emergent network architecture. This presents a considerable challenge for network security, especially in terms of how hardware (such as sensor networks and actuators) and mobile devices can be trusted sufficiently to join existing networks.

Current research in this area has identified multiparty authentication as a key challenge for the IoT. Whilst multiparty models have been trialled in multi-cloud environments, the additional complication of authenticating constrained hardware devices presents an interesting and worthy area of investigation that has the potential to make a substantial contribution to the research community.

This PhD project will develop a multiparty authentication model for distributed IoT networks. There will be a need to develop proof-of-concept simulations for the purposes of benchmarking and evaluation, as well as a hardware-in-the-loop demonstrator. Applicants should be comfortable with programming in a high level programming or simulation language (C, Python, R, etc.). A background in mathematics/statistics and network security will be useful, as will experience of interfacing embedded systems and sensors.

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There is a necessity to consider aspects of security when designing software applications. A contributing factor is the increasing legal and financial pressures software developers are under should their software be left vulnerable. This is further exacerbated by the ease of performing large-scale automated software attacks. There are many best practice guides and standardised design patterns that can be followed to ensure a high-level of security is maintained, but such advice is often provided by a subject expert. This research aims to investigate whether learning what an adversary looks for to determine whether a system is vulnerable, as well as how they attack a software system, can be used to build-in simple deterrents that may ultimately increase security with very little software development effort. This project aims to leverage the fundamental philosophy from ‘secure-by-design’ research within crime prevention and construction sectors. It is foreseen that this project will require the input from cyber criminals to determine what they look for within a software system to determine if it is worth attacking. This will then inform a phase of research into establishing key recommendations to consider during software design to prevent the likelihood of being attacked. Case studies will then be performed to evaluate the developed approach.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

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Supervisors

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Outline

As the Internet of Things (IoT) continues to expand, there are more Internet-connected IoT heterogeneous devices and applications to be exploited. IoT security and privacy has proven to be a major challenge in the IoT industry. There are different types of attacks and threats that may diffuse the IoT architecture, such as spoofing attacks, distributed denial of service attack, botnets, malicious code injection, replication attacks, and eavesdropping. To resist malicious behaviour, the IoT requires a more holistic architecture for addressing trust, confidentiality and privacy issues. This PhD project will identify security risks in IoT networks and focus on developing new, smarter, and more agile security approaches in IoT. There will be a need to develop proof-of-concept simulations for the purposes of benchmarking and evaluation, as well as a hardware-in-the-loop demonstrator. Applicants should be comfortable with programming in a high level programming or simulation language (C, Python, R, etc.). A background in mathematics/statistics and network security will be useful, as will experience of interfacing embedded systems and sensors.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

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Outline

The notion of the Digital Twin, which is a digital replica of a physical entity, has become prevalent in various domains including manufacturing, urban planning, healthcare and transport. Digital Twins can be considered as a mirror reflection of the physical object, maintaining and providing an up to date status of their physical counterpart. Such functionality abstracts access to the physical device, which normally has limited resources, supporting enhanced capabilities such as queries by application agents, status visualisation, simulation of alternative scenarios and advanced diagnostics. This highlights the need for well-defined descriptions of Digital Twins and their physical counterparts in order to enable seamless communication, interoperability and data integration from heterogeneous sources. Such challenges can be addressed through the use of semantic technologies. Thus, Semantic Digital Twins become prevalent for overcoming future challenges and driving innovation across a wide range of associated fields.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

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Outline

The Internet of Things (IoT) is the vision of a network of physical objects (“things”) The Internet of Things (IoT) is the vision of a network of physical objects (“things”) equipped with sensors, software and networking capabilities which enable these objects to collect and exchange data. The PhD project would investigate approaches and develop novel methods for (a) enriching IoT data by linking them to ontologies and other data and information sources, and (b) providing reasoning services for processing IoT data at a high level of abstraction. Addressing (a) would be a major step towards achieving a Web of Things which would be siting on top of IoT functionalities (just like the WWW is residing on top of the Internet). Addressing (b) would enable the intelligent processing of huge amounts of IoT data, and requires to overcome major challenges in terms of the size and dynamicity of IoT data, among others. The project is suitable for a PhD student who has already acquired significant knowledge on semantic and knowledge technologies, e.g. in the areas of semantic web, linked data management, knowledge representation and reasoning, or logic programming. equipped with sensors, software and networking capabilities which enable these objects to collect and exchange data. The PhD project would investigate approaches and develop novel methods for (a) enriching IoT data by linking them to ontologies and other data and information sources, and (b) providing reasoning services for processing IoT data at a high level of abstraction. Addressing (a) would be a major step towards achieving a Web of Things which would be siting on top of IoT functionalities (just like the WWW is residing on top of the Internet). Addressing (b) would enable the intelligent processing of huge amounts of IoT data, and requires to overcome major challenges in terms of the size and dynamicity of IoT data, among others. The project is suitable for a PhD student who has already acquired significant knowledge on semantic and knowledge technologies, e.g. in the areas of semantic web, linked data management, knowledge representation and reasoning, or logic programming.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

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Outline

In practice, software architectures are often documented after implementation. Architectural Reconstruction and Recovery (ARR) aims to reverse engineer the software architecture from system artefacts in order to facilitate analysis and understanding. ARR approaches are either based on behaviour-based clustering techniques such as weight or program slicing or based on filtering methods, which recover partial descriptions of the architecture. However, current techniques are limited to extracting code-level information, not architecturally significant abstractions. In addition, the approaches are context-dependent, and no method focuses on those aspects of the architecture which address software qualities. New approaches are required to recover relevant views of the software architecture, including architectural design decisions, that map to architectural concepts, i.e. patterns and tactics.

The aim of this project would be to investigate the efficacy and limitations of existing architecture recovery and reconstruction approaches in recovering relevant architectural views, including architectural design decisions, leading to the design of new, enhanced or hybrid techniques.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

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Outline

Architectural Consistency (AC) aims to measure the architectural drift or erosion of the implemented architecture against the designed architecture. Several AC techniques have been proposed, which can be classified based on how they extract the implemented architecture using static or dynamic code analysis. However, these approaches have several limitations, including being driven by the codebase, not the architecture, assessing the impact of architectural flaws on the recovered architecture, and the difficulty in quantifying architectural inconsistency effects. The aim of this project would be to investigate the effectiveness of existing architecture consistency approaches in quantifying the impact of architectural inconsistency effects leading to the design of new, enhanced or hybrid techniques.

Funding

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Outline

Architectural-Level Metrics (ALM) aim to provide a quantitative indicator of the quality of software architecture by indicating hot-spots and architectural flaws in the design. While there are a number of methods and techniques for measuring and understanding code complexity issues, identifying code smells and technical debt, their applicability at the architecture level are inconsistent and not proven. By addressing software sustainability at the architectural level, it allows the inhibiting or enabling of systems quality attributes, reasoning about and managing change as the system evolves, predicting system qualities, as well as measuring architecturally significant requirements.

The aim of this project is to investigate the fitness of architectural-level metrics in identifying architectural flaws and quantifying software architecture sustainability leading to the design of new or improved measures and metrics.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

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Outline

In collaboration with the Western Norway University of Applied Sciences, this PhD project is associated with the recently established SmartOcean centre for research-based innovation funded by the Norwegian Research Council and several industry partners (https://sfismartocean.no). The overall vision of the SmartOcean centre is to create a flexible, robust, energy efficient, and cost-effective smart sensor network platform for marine measurement and data handling. An important part of this vision is to develop a multi-tiered architectural software system framework enabling innovation and cost-effective development of robust software applications and data services. The base of the framework will be internet-of-things, cloud computing services, and the emerging web-of-things paradigm. The goal of the PhD project will be the initial design, prototyping, and validation of an architectural framework in collaboration with industry and research partner institutes in the ocean domain. Aspects of the PhD work may also involve evaluation of existing research-based architectural frameworks (e.g. SysML, DANSE, COMPASS, and AMADEOS) for engineering and validation of system-of-system-based architectures within the context of a smart ocean.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

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Outline

Railway ballast is a granular structure critical to the integrity and stability of rail track. A holistic approach to modelling of ballast and ballast stones can be considered as a multiscale problem. It needs to account for both the mechanical interaction amongst the stones, and their weight bearing properties against larger structures such as sleepers. Existing methods: analytical methods are restricted to much simplified models where ballasts are approximated as linear springs. Finite Element Methods (FEM) can offer macroscopic behaviours at great details but lacks ability to model the mechanics amongst individual constituent stones. Discrete Element Methods (DEM) are suitable for modelling the dynamics amongst ballast stones. However, the stability of the simulation lies in careful consideration of integration methods as well as handling of contact. Moreover, an accurate geometric representation of railway ballast requires stones to be adequately meshed.

Proposed Investigation:

The successful candidate will investigate the following areas: * Adequate solid representation and meshing for ballast stones * Develop and apply a stable discrete element method using an off-the-shelf physics engine * Parallelisation of discrete element method to increase its computational throughput for real-time simulation

This interdisciplinary project is suitable to candidates from both engineering and computing background. Strong programming skills and knowledge in numerical methods are highly desirable.

Funding

Please see our Research Scholarships page to find out about funding or studentship options available.

Deadline

Our standard University deadlines apply. Please see our Deadlines for Applications page to find out more.

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Computing has a vibrant and rapidly growing research community with expertise in diverse areas, for example visualisation, information and systems engineering, and intelligent systems.

Our aim is to research and develop new methods and technology in computer science that will have a real impact on global grand challenges in areas such as transport, health, security and energy.

There is a wide range of topics which can be researched, including the following research areas:

[] Artificial intelligence: planning, autonomous systems, knowledge representation and reasoning [] Information systems: Web-based information systems, semantic web, big data [*] Human-Computer Interaction: visualisation, computer games

In the past, research has been conducted in collaboration with prestigious national and international partners from academia (e.g. Oxford, UCL, Bristol, Newcastle, Stanford, Bologna, VU Amsterdam, Vienna) and industry (e.g. British Telecom, IBM, Schlumberger).

To find out more about the research we conduct, take a look at our Research, Innovation and Skills webpages, where you will find information on each research area. To find out about our staff visit ‘Our experts’ which features profiles of all our academic staff.

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Researcher Environment

To make a formal application, complete the online application form.

This normally includes the submission of a research proposal. Read through the [proposal guidelines|http://www.hud.ac.uk/researchdegrees/writingaresearchproposal/] first to make sure you cover all the information needed, and ensure you include the proposal (if required) when submitting your online application. You can check whether the degree you are applying for requires a proposal by checking the specific course entries.

If you wish to be considered for a scholarship, please read through the scholarship guidance and include the name of the scholarship on your online application.

Applications are assessed based upon academic excellence, other relevant experience and how closely the research proposal aligns with Huddersfield’s key research areas.

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