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Computer Science and Informatics (PhD)

2023-24 (also available for 2022-23)

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

Start date

2 October 2023

15 January 2024

16 April 2024

Duration

The maximum duration for a PhD is 3 years (36 months) full-time or 6 years (72 months) part-time 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 October 2023

09 June 2023 for International and Scholarship students

30 June 2023 for Home students

For January 2024

20 October 2023 for International and Scholarship students

17 November 2023 for Home students

For April 2024

26 January 2024 for International and Scholarship students

23 February 2024 for Home students

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, normally with a classification of merit or distinction, 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. Read more about the University’s entry requirements for students outside of the UK on our Where are you from information pages.

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

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 Large Hadron Collider at CERN is the largest and most important project in particle physics in the world. It is currently being upgraded to increase its potential for discovering new physics. This upgrade will increase the amount of beam in the accelerator. To make this possible, the accelerator collimation system must also be upgraded. This is a vital part of the accelerator that removes particles that would otherwise be lost from it and possibly cause damage to it. This upgrade will be very challenging.

The PhD will investigate new forms of collimation that would help to make this upgrade possible. It will involve the development of a software tool called Merlin, including comparisons with measurements made at CERN, and its use for studying and optimising the new collimation techniques.

The work will be done as part of an UKRI-STFC and CERN funded project and will include collaboration with the University of Manchester and CERN.

Funding

This is a fully funded project with the cost of tuition fees and an annual bursary (£15,285 in 2020/21) funded through the Science & Technology Facilities Council.

Deadline

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

Supervisors

How to apply

Outline

CERN is a European research organization whose primary function is to provide the particle accelerators and other infrastructure needed for high-energy physics research. ATLAS is the largest, general-purpose particle detector experiment at the LHC designed to observe phenomena that involve highly massive particles and to search for evidence of theories of particle physics beyond the Standard Model. The continuing programme of upgrades and consolidations to the LHC and the experiments at CERN has resulted in massively increased ICT demands in both storage and computational capacity as well as added complexity.

Middleware is a way to manage this complexity as it provides common services and capabilities to applications outside of the operating system including data management, application services, messaging, authentication, and API management. The success of integration PaaS (iPaaS) has led to the emergence of Middleware as a Service (MWaaS), which is a cohesive suite of integration services aimed at hybrid integration use cases, including service integration, API-led integration and API management, front-end and back-end integration, and application and data integration.

Funding

This is a fully funded project with the cost of tuition fees and an annual bursary (£16,062) funded through the Department of Computer Science, University of Huddersfield.

Deadline

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

Supervisors

How to apply

Outline

Computer Science at the University of Huddersfield are a Technical Associate Institute of the ATLAS project at CERN. ATLAS is the largest experiment at CERN and is most well-known for the role it played in the discovery of the Higgs Boson. As a part of the High Luminosity upgrade of the Large Hadron Collider (LHC) the ATLAS detector is also being upgraded which will require the processing of very high raw data rates (>5 TB/s) in order to identify events that could contain interesting physics that will then be stored for offline detailed analysis. We are offering two funded PhD studentships to work in this area. We envisage two main areas of activity with the detailed workplans being formulated based on the interests of the successful candidates. The two areas of activity are: 1) Development and characterisation of novel data processing pipeline approaches that can filter the very high data rates off the detector and implement algorithms that can detect events of interest. This project would suit individuals with an interest in high-speed communications systems; FPGA, GPU and other advanced processing solutions (e.g. MPSoC); and modelling and simulation of such systems. 2) Development and characterisation of algorithms to detect events of interest in the filtered data. Traditionally, techniques such as the Hough Transform have provided the basis for the detection, but there is significant interest now in the application of machine learning to this problem area. This project would suit individuals with an interest in pattern detection and machine learning. Both of the projects will involve close collaboration with, and visits to, our ATLAS partners located at prestigious, globally-distributed universities and research facilities, and there will be opportunities to spend time at CERN in Geneva.

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

This study will build on existing research mapping twenty-first-century skills to degree provision as the basis of a microcredentialing standard for UK higher education. This work considers a broader application of a microcredentialing standard across formal education.

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

This project will investigate the design and development of a new consensus protocol over sparse networks for distributed ledgers. Byzantine fault tolerance (BFT) has been proposed and implemented as a consensus protocol for distributed ledgers. Sparse networks may better model peer-to-peer communications as used in most ledgers. Therefore, the goal of this project is to investigate whether BFT over sparse networks might function as a better consensus protocol with greater transaction throughput. BFT over sparse networks is considerably harder than the complete network case. Work has been conducted in this area but existing results appear to be impractical in terms of number of protocol rounds and the size of the protocol message. The project would intend to produce a more efficient protocol in terms of number of rounds and protocol message size.

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

How to apply

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

How to apply

Outline

Computer Science at the University of Huddersfield are a Technical Associate Institute of the ATLAS project at CERN. ATLAS is the largest experiment at CERN and is most well-known for the role it played in the discovery of the Higgs Boson. As a part of the High Luminosity upgrade of the Large Hadron Collider (LHC) the ATLAS detector is also being upgraded which will require the processing of very high raw data rates (>5 TB/s) in order to identify events that could contain interesting physics that will then be stored for offline detailed analysis.

We are offering a funded PhD studentship to work in this area of compute acceleration for the event filtering and track reconstruction of the ATLAS TDAQ (Trigger & Data AcQuisition) system. Track reconstruction consists of a chain of data and compute intensive stages including clusterisation and track fitting. This project aims to develop novel compute architecture using heterogeneous processors to increase the throughput of the tracking chain. Additionally, there are also opportunities to addressing the challenges from a combination of algorithms and hardware acceleration models.

This project will involve close collaboration with, and visits to, our ATLAS partners located at prestigious, globally-distributed universities and research facilities, and there will be opportunities to spend time at CERN in Geneva.

Funding

Funding: This is a fully funded project with the cost of tuition fees and an annual bursary £16,062 funded through the Department of Computer Science, University of Huddersfield.

Deadline

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

Supervisors

How to apply

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

How to apply

Outline

It is increasingly important that the skills gained within learning are clearly mapped to job roles and that they are communicated clearly to employers, governments and educational providers. Using a 21st Century Skills framework this research will explore a range of courses and job roles to categorise learning and to therefore support learning analytics approaches based on digital skills development.

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

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

How to apply

Outline

A major challenge of additive manufacturing (AM) technology is that AM processes are not robust enough and AM production machines lack sufficient process control, which consequently bring various shortcomings that are commonly seen in AM products, such as poor as-built surface finish, porosity, and mechanical properties not equivalent to those of bulk materials. X-ray Computed Tomography (XCT) is often employed to inspect the porosity of AM parts, providing more information (e.g. size, location, morphology) in comparison to the traditional Archimedes method. However, the accuracy and performance of XCT on AM porosity inspection need to be verified. This research project will conduct an experimental study to target the optimum scanning configuration for AM porosity measurement and compare with other porosity inspection methods, e.g. Archimedes, scanning electron microscope and ultrasonic. XCT simulation will complement to experimental work, which allows investigate the impact of major scan parameters on XCT porosity measurement. Physical and virtual AM artefacts will be developed. The project will be based on the Future Metrology Hub, Centre for Precision Technologies. The selected student will be provided with the training of XCT and simulation software.

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 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

How to apply

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

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

Supervisors

How to apply

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

How to apply

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

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

Deadline

Supervisors

How to apply

Outline

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.

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

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.

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

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).

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 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 self-regulation about all this? To address these questions, this study will investigate personalisation in learning and learning analytics in relation to this from a self-regulated learning perspective. Through a human factors and ergonomics approach, prototypical self-regulated learning systems will be analysed and compared, and the mechanics that define personalisation of self-regulated learning will be modelled. These mechanics will then be analysed to identify measurable indicators of personalisation and self-regulation 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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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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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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Which techniques best facilitate the enforcement of authorisation policies on permissioned distributed ledgers? This project intends to determine if there are alternative approaches to those currently used to ensure data privacy. In particular, it will investigate whether particular ledger data structures better facilitate the enforcement of authorisation policies on which participants can read and update data in the ledger? Currently, external access control, separate public-private key pairs for each permission granted, and attribute certificates have been proposed. External access control is only possible in private ledgers and public key-based systems rely on trust that the keys are genuinely associated with a stated identity. Unfortunately, large public ledgers lack a mutually trusted central key store. We intend to investigate the efficacy of other methods of ledger authorisation. The goal will be to provide lightweight cryptographic methods and data structures to achieve this.

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Currently, authorisation policies on permissioned distributed ledgers are primarily enforced by an access control system which is external to the ledger, such as OS access control, or by the use of a specific key pair for each permission. The former relies on measures external to the ledger and the latter requires a large set of keys to manage. Therefore, this project aims to devise and develop new lightweight methods which are part of the ledger system which can be used to enforce authorisation policies in permissioned distributed ledgers.

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This project will Investigate storage of a subset of a blockDAG on a client while maintaining consistency with the global DAG. WP2 will investigate whether a subset of the transactions can be stored on a client while maintaining consistency. Many distributed ledgers require participants to store large amounts of data to maintain consistency with other participants. However, devices of limited storage, such as cellphones and IoT devices, may be incapable of storing the full chain or graph of transactions. We intend to determine if storing a subset of this data will still allow participants to remain consistent with the global ledger state and not introduce any threats to the integrity of the ledger. For example, is it possible to only store a subgraph of a blockDAG and maintain consistency?

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As a a result of the cryptographic properties of Blockchains and other distributed ledgers, it is required that participating clients store the longest transaction chain or directed acyclic graph of transactions in order to validate existing transactions and remain consistent with other participants. However, as the number of participants and transactions increases, this poses a significant scalability issue. IoT devices, in particular, have limited storage capacity making this issue particularly critical. Therefore, this project will investigate what techniques can be employed or devised to reduce the storage requirements to store ledger history, particularly on limited storage devices such as IoT.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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The University of Huddersfield and Schlumberger Cambridge Research Limited offer a fully funded PhD position in the field of Automated Planning within Artificial Intelligence.

With the PhD project, we will advance the state of the art of automated planning applied to real-world problems, and have a significant impact to the operations of Schlumberger. One of the key challenges in a global engineering operation is optimal cooperation and coordination which happens on all scales: from in-country local experts to regionally sourced consumables and additives to global deployment of small numbers of highly specialized measurement and drilling systems. Planning takes place on all scales and must be reactive to replan under changing conditions. As we automate more of these activities, through AI planning and scheduling, there is an opportunity to explore the coupling between problems on overlapping domains and to develop the algorithms that will understand the dependencies; providing solutions to all that are consistent and optimal in a global sense.

We are looking for motivated and talented applicants with very good university degrees (MSc or equivalent) in Computer Science or Mathematics (or comparable fields), and with solid background in one or more of:

  • automated planning and scheduling,
  • knowledge representation and reasoning, -formal methods in computer science, -strong implementation skills motivated by challenging combinatorial problems and interest in theory.

The positions are co-funded by Schlumberger Cambridge Research and the Engineering and Physical Sciences Research Council (EPSRC), full-time, and fixed-term for three years. Candidates should be independent in organising their work, keen on working with teams of experts of project partners, and fluent in English, oral and written. Starting time can be as soon as possible, according to the University of Huddersfield PhD program starting dates.

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Brain-computer interfaces are becoming more accessible and accurate in their capture of data with much research in their application and understanding the data captured. Human Computer Interfaces for playing games also evolve at a rapid pace with myriad solutions on the market. Recent advances in understanding input without traditional controllers/mouse/keyboard are leading to exciting experiments, for example the Leap Motion controller and hands-free VR in the Occulus Quest. This project aims to explore the possibilities afforded by modern brain-computer interfaces and their potential application in gameplay and decision making. The aim is to provide answers to the following questions: - Are affordable grade brain-computer interface devices accurate and consistent enough to be used to understand player decisions? - Can a framework be developed to offer consistent input mechanisms for player input from a brain-computer interface? - What new game mechanics can be developed using this innovative interface?

Research findings will be important to the global game development community in both research and commercial context for real-world applications. The applicant will need a strong understanding of game design theory and either Unreal Engine 4 or Unity in order to implement the systems and conduct experiments.

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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.

Student support

At the University of Huddersfield, you'll find support networks and services to help you get ahead in your studies and social life. Whether you study at undergraduate or postgraduate level, you'll soon discover that you're never far away from our dedicated staff and resources to help you to navigate through your personal student journey. Find out more about all our support services.

Researcher Environment

We provide a supportive and vibrant research environment for postgraduate researchers (PGRs). Researchers at all levels are encouraged to contribute and collaborate. The Graduate School ensures that postgraduate research is of the highest quality and ensures you have the training and resources that you need to become a successful researcher.

We have an exciting and comprehensive Researcher Skills Development Programme available to all postgraduate researchers. This enables you to broaden your knowledge and access tools and skills which can significantly improve employability. The programme is also mapped onto Vitae’s Researcher Development Framework (RDF), allowing you to benefit from Vitae’s career support as well as our own programme. We also have a team dedicated to improving the academic English needed for research by our international PGRs.

Our training is delivered in a variety of ways to take advantage of technology platforms as well as face-to-face workshops and courses. The University subscribes to a programme of online research training support accessed through a dedicated researcher module in Brightspace, the University’s Virtual Learning Environment. We also subscribe to the University of East Anglia webinar series and The Good Doctorate video training series. We are part of the Northwest and Yorkshire PGR Training Group that enables PGRs to attend relevant training opportunities at other nearby universities. We also hold a series of PGR focussed events such as 3 Minute Thesis, the PGR led research conference and regular informal events throughout the year.

Important information

We will always try to deliver your course as described on this web page. However, sometimes we may have to make changes as set out below.

When you are offered a place on a research degree, your offer will include confirmation of your supervisory team, and the topic you will be researching.

Whilst the University will use reasonable efforts to ensure your supervisory team remains the same, sometimes it may be necessary to make changes to your team for reasons outside the University’s control, for example if your supervisor leaves the University, or suffers from long term illness. Where this is the case, we will discuss these difficulties with you and seek to either put in place a new supervisory team, or help you to transfer to another research facility, in accordance with our Student Protection Plan.

Changes may also be necessary because of circumstances outside our reasonable control, for example the University being unable to access its buildings due to fire, flood or pandemic, or the University no longer being able to provide specialist equipment. Where this is the case, we will discuss these issues with you and agree any necessary changes.

Your research project is likely to evolve as you work on it and these minor changes are a natural and expected part of your study. However, we may need to make more significant changes to your topic of research during the course of your studies, either because your area of interest has changed, or because for reasons outside the University’s control we can no longer support your research. If this is the case, we will discuss any changes in topic with you and agree these in writing. If you are an international student, changing topics may affect your visa or ATAS clearance and if this is the case we will discuss this with you before any changes are agreed.

When you enrol as a student of the University, your study and time with us will be governed by the University’s Terms and Conditions and a framework of regulations, policies and procedures, which form the basis of your agreement with us. It is important that you familiarise yourself with these as you will be asked to agree to abide by them when you join us as a student. You will find a guide to the key terms here, along with the Student Protection Plan, where you will also find links to the full text of each of the regulations, policies and procedures referred to.

The Office for Students (OfS) is the principal regulator for the University.