Computer Science and Informatics (PhD)

2019-20 (also available for 2020-21)

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

Start date

23 September 2019

13 January 2020

6 April 2020

Duration

The maximum duration for a full-time PhD is 3 years (36 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.

Application deadlines

For PGR start date January 2020

29 November 2019

For PGR start date April 2020

11 February 2020

For PGR start date September 2020

02 July 2020

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.

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 CACHR project seeks to advance the fields of cognitive robotics and embodied cognitive science by developing a robot control system based upon an empirically grounded computational model of the human mind – a cognitive architecture (CA). The characteristics of human cognition, perception, locomotion and sensorimotor control (e.g., slow, stochastic, predominantly parallel, memory-based processing, attentional bottlenecks, automatic and constant learning, sub-optimal satisficing, forgetting etc.) are very different from AI mechanisms. It is an important question – not only for robotics but for cognitive science more broadly – to understand how these properties enable successful intelligent human behaviour and whether they may be used to support the same behaviour in robots. The project will require a number of technical and theoretical problems to be solved. The primary challenge will be to develop an interface between the NAO robot and the ACT-R cognitive architecture (via the ROS middleware) to translate between the symbolic knowledge representations used by the CA and the data created and used by the robot. When the initial development phase is complete, the effectiveness of the system will be tested in a series of experiments to investigate the robot's cognition and behaviour under the constrained control of the cognitive architecture.

The project represents an ambitious, cutting edge conception that will allow a range of important technical and theoretical questions to be addressed concerning the benefits of applying the defining features of human cognition to the design of robots with general intelligence and also the challenges of requiring a cognitive architecture to deal with the real-world constraints imposed by embodiment.

The project aligns well with UK Government, European Commission, University, and School priorities relating to Robotics and Autonomous Systems (RAS) research and represents a novel and highly innovative multidisciplinary and cross-school approach to cognitive robotics that will provide a dedicated, long-term research application for the new NAO robot, and integrate PARK's existing expertise in AI methods with broader cognitive science theories and approaches. As such, the project will be of interest to other academic and industrial researchers working in cognitive and developmental robotics, for example, the developmental robotics research lab at Aldebaran Robotics. To support the PhD student's experience and skill set on knowledge transfer and R&D in the robotics industry, contact will be made with Aldebaran (and other appropriate institutions e.g., the Centre for Robotics and Neural Systems at Plymouth University) to establish research links and propose visits.

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 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

Digital investigations involve the analysis of data to determine hypotheses, which are subsequently evaluated through further analysis. Investigations can vary from performing a routine penetration analysis to a large scale public authority examination. A fundamental aspect of a digital investigation is maintaining data integrity and traceability. This is something that becomes more difficult the longer the investigation is on going. There is also the necessity to arrive at a conclusion as soon as possible, especially when analysing criminal activity. Given that time is so important to a digital investigation, the burden of wasting unnecessary time pursuing potential hypotheses can have a significant impact. This research project aims to develop intelligent hypothesis suggestion mechanisms within specific areas of digital investigations (I.e. penetration testing). It is foreseen that this will involve the investigation and selection of specific investigative process; the development of algorithms and heuristics to suggest potential hypotheses; and the development of software tools enabling end-user adoption. Empirical analysis will be performed through stakeholder evaluation are real-world case studies.

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 availability of cheap RGB-0 sensors has led to an explosion over the last five years in the capture and application of colour plus depth. This resulted in a demand for robust and real-time processing of data that is typically noisy and incomplete. This project will investigate the state-of-the-art and key techniques in enabling real-time segmentation and feature learning from RGB-0 data, saliency-based approaches to 20/RGB-O registration, tracking and fusion from multiple sensors, point-cloud processing and 30 reconstructions, illumination and realistic rendering, robust RGB-0 SLAM, etc. Novel computational and visualisation models and techniques will be devised to facilitate applications such as in digital heritage, action and activity recognition, occluded 30 scene reconstruction, 40 modelling of dynamic shapes, and VR/AR system integration.

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

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

VR-based simulations for medical procedure training with haptic–visual–audio feedback have been widely studied. There are also pilot projects for applying VR in the management of pain in the rehabilitation process for burn patients. The objects and clinical scenes in these VR settings are often computer generated polygonal models that lack of the real world bearings such as the similarity on appearance, sense of scale, and harmony of light. Augmented Reality (AR), on the other hand, offers nature solutions to many of those challenges through integrating real and computer-generated models, which can provide precise measuring and reference information that are appropriate to medical applications such as neurosurgical planning. This project intends to investigate the new found power of prevailing consumer grade AR technologies that are often first reported in game industry. The study will focus on their collaborative and training potentials in facilitating patient understanding, clinical trials, and nursing practices. The visual signal fusion qualities of AR will be explored to enable a solution combining realistic treatment scenarios and interactive in-body experiences for training medical staff. It is anticipated that such a research will benefit the medical and healthcare sectors as a whole.

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

Automated planners (i.e. software capable of choosing and organising actions by anticipating their expected outcome) are being increasingly used to automate real-world planning problems. For example, logistic problems, traffic management, and in planning for engineering processes. Although current automated planners are capable of solving a problems with a large array of characteristics (temporal, numeric, etc.), they struggle to plan for domains which are numerically rich by nature (e.g. containing non-linear resources). This often results in the requirement for in-depth knowledge to model real-world domains, and can even result in the technology been deemed unsuitable for the application. This project will investigate how current state-of-the-art in automated planning theory can be further developed to overcome this limitation.

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 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

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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

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Outline

Changepoint analytics is so very fundamental for all domains of data analytics. Data, i.e. the signals at issue, may be multivariate. with noise. Resolution scale is addressed with wavelet and related transforms. Also at issue are multiple changepoints. Packages in R include factorcpt and changepoint. Applications are many and varied, and include all manufacturing and related production contexts, the rapidly growing domains of Internet of Things, Smart Cities, automated transport systems. Prominent literature includes ocean engineering, gravitational wave analytics (from LIGO, Laser Interferometer Gravitational-Wave Observatory), financial data. Relevant here is also clusterwise regression, and our developed regression with inherent hierarchical structure of highly complex systems. The latter, ultrametric regression, is in our published work on Colombian historical social violence. A further possible aim is to relate this work to conformal prediction, a very important machine learning prediction method.

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

The NHS prioritise the purchasing of medical devices solely based on function. It appears that little, if any, attention is paid to the security of these devices. Often, purchasing of these devices is left to the discretion of the medical staff. For example, in the case of implants such as pacemakers, it is the responsibility of the patient's doctor, to look into the security of these devices. It appears unreasonable to require a medical practitioner, who specialises in the diagnosis and treatment of specific ailments, to also have the required knowledge of cyber security principles to adequately judge if a device is secured against potential cyber-attacks. Thus, such a regulation needs to be amended to ensure the security of the medical devices.

This project aims to develop a framework which establishes the baseline for the expectations of the security within all medical devices specifically tailored to the field of medical IoT. The final product of this project would be a system that would flag up and predict potential vulnerable devices based on disclosed vulnerabilities, as well as confidential threats reported previously by the manufacturer to the NHS. Thus, this system would need to enable any member of staff instantly to find out whether there was a potential or known issue within any piece of medical equipment. The system would also ensure that patients with fitted implants or those about to undergo medical treatment will be safe against cyber attacks/cyber terrorism.

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

Beam loss monitors are used at all high energy and high intensity particle accelerators to protect the machine from damage, optimise operation and lower ambient radiation levels. These are often based on a distributed system of individual detectors, each covering a small length of the overall accelerator.

Fibre based monitors are a promising alternative to such local monitors, allowing full coverage of losses throughout critical regions. Two approaches will be considered: the use of scintillating fibre where the fibre itself will generate an optical signal in response to radiation from the beam and then convey the signal to a detection point; or a fibre that utilises Cherenkov radiation generated in the fibre which is also conveyed to a detection point. Localising these losses from such monitors in circular or multi-bunch machines nevertheless involves disentangling the complicated beam loss patterns at the detectors. This PhD will design and build a detection system capable of acquiring such data, allowing coincidence or time-of-arrival measurements. It will also investigate the data processing techniques required to reconstruct the loss locations, including the use of machine learning techniques to interpret the patterns observed and to identify the existence and location of potential beam loss events.

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

We live in a complex world, defined by dynamics of constant change, and characterised by unpredictability, unknowability and uncontrollability. The inability to cope with complexity leads to succumbing to wicked problems, with tragic consequences on human development. “Complexivist” mindsets are required to cope with complexity. In order to prepare individuals for an increasingly complex and intertwined world, contemporary education should foster the ability to adapt to change, to understand phenomena in context, to face ill-defined situations and to work in collaboration with others who may not share ideas or interests. Furthermore, deep learning should be supported and facilitated also in informal environments, where engagement with complexity can be higher and deeper.

This project studies the domain of learning, with the aim of developing novel adaptive educational strategies and instruments to leverage complexity science, information technologies and game-based interaction design techniques to: • Promote learners’ engagement with complex problems • Foster learning and the development of “complexivist” mindsets in formal and informal environments.

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

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

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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

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Supervisors

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Outline

Understanding and acting upon sustainability is essential for the development of our global world. In 2015 the United Nations released an agenda of 17 key goals to be achieved by 2030 in order to transform our world in a properly sustainable environment. These goals relate to key social, environmental, economic and cultural problems, covering issues such as: poverty; hunger; health; well-being; gender equality; education; availability and management of water supplies, food and energy; employment; economic growth; climate change; etc.

This project addresses selected targets of the 2015 UN agenda through developing the following research lines: • Fostering the development of complex problem solving and systems thinking skills through the use of games and simulations • Enhancing healthcare, social care and quality of life for sufferers of high social impact diseases (e.g. dementia) and disorders (e.g. autism), through the use of adaptive assistive technologies and game-based engagement strategies • Enhancing environmental education and public awareness through the use of social games, simulations and augmented reality technologies • Fostering social awareness and engagement through the use of social media and interactive entertainment technologies • Fostering learning and education for sustainable development in formal and informal environments, through the use of social media and interactive entertainment technologies • Investigating sustainability behavioural patterns through leveraging big data, machine learning and context-aware computing.

Funding

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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

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Deadline

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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

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

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Deadline

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Supervisors

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Outline

Recent years have witnessed the fast emergence of massive graph data in many application domains, such as the World Wide Web, linked data technology, online social networks, and Internet of Things (IoT). Most graphs are generally subject to changes in terms of connections and nodes. For example, the emerging Internet of Things calls for graph data management with connection and node changes because smart things are normally moving and their connectivity could be intermittent, leading to frequent and unpredictable changes in the corresponding graph models. This project aims to explore what influence can be brought by changes (including both incremental and decremental changes) in the underlying graph models of big IoT data. New algorithms will be designed to identify and manage the most influential connections and nodes in an IoT data graph model (Some initial work has been presented at DASFAA 2017). The success of this project will be able to help manage the dynamicity of an IoT system effectively and allocate resources and budgets wisely to the most critical parts of the system.

Funding

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Deadline

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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

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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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Beam diagnostics based on electro-optical techniques using the Kerr effect in birefringent crystals and CW lasers would offer the possibility to measure beam induced fields with an extremely large bandwidth, ranging from DC to tens of GHz.

Measurement of DC signals is of particular interest for precise positioning of coasting particle beams extracted for next generation fixed target experiments. This project will study the capability of E-O crystal to measure low amplitude DC fields. It will involve developing a dedicated test bench to measure and compare different types of E-O material and selecting candidate technologies. An optimised optical detection syst em with a response to DC will be developed and characterised. Post detection processing techniques using machine learning techniques will be developed to generate the beam diagnostic data required by the operators, and the implementation of these algorithms in FPGA-based systems to meet the processing speed requirements of the application will be explored. The study will also investigate the design and integration of such a monitor on a beam line at CERN.

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Artificial intelligence is widely used under a ‘black-box’ philosophy within engineering processes aimed at automation and optimisation. For example, path planning is used to determine optimal cutting paths for CNC machine tools and neural networks are used to predicting thermal behavior of machine tools. Although the underlying technology will change for different applications, the requirement to capture, extract and utilise domain knowledge is consistent throughout. This will involve a domain expert with in-depth knowledge to determine what knowledge is required, and how it can be encoded in a suitable form for AI. This creates a ‘bottleneck’ in AI utilisation as the implementation can only ever be as good as the extracted knowledge. This project will investigate and develop tools to assist in knowledge extraction and analysis to minimise the requirement on expert knowledge.

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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 the role of data diagnostics within the context of personalised learning. This research study will investigate this topic using a data-driven approach to learning analytics, in particular considering the potential role for automation within learner support through the use of learning analytics and models of learning success.

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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 human behaviour within the context of personalised learning. This research study will investigate this topic using a mixed methods approach to analyse both data-driven and behaviour-driven analytic techniques. In particular it will consider the role of initial and early levels of learning engagement as a success indicator and therefore its relative role in learning success.

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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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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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Complex scene understanding has been widely acknowledged as the ultimate aim of computer vision systems and an enabler for general purpose intelligent robotics. Accompanied by advancements in Artificial intelligence, biological vision and cognitive science, as well as engineering feasts in micro-processors , miniature sensors, and network and communication technologies, it is anticipated that this technology integration trend will bring economic and societal benefits in terms of new products, automation, smarter ways of working, better security monitoring and assistive technologies. The aim of this project is to investigate the utilisation of machine learning (ML) technology for processing and validating sensor signals to produce information that could be useful in high-level cognitive models. The project's results would be evaluated by application within a humanoid robot setting. The project will start from the creation of a general purpose, integrated and flexible machine vision system that can facilitate high-level decision making in complex circumstances. The anticipated challenges include augmenting additional signal sources, boosting critical signals in a noisy environment, and the creation of a feasible machine learning framework for the automatic compilation of symbolic knowledge.

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Large data sources will be provided by ANT Ltd. (Advanced Nano Technologies, www.antsltd.com). The major focus of this project’s work is the integration and synthesis of data sources, here especially at micro and nano resolution scale of spectroscopy signals. Central here is how entropy determines the relevant resolution scale of the spectral signals, so multiscale entropy is at issue. Entropy has also been used for calibration and for overall system use. The context for this work is innovative drop technology that responds to a light beam. Calibration of the spectral signal thus formed is used for assessing and evaluating the drop’s content. Such drop technology sensors and diagnostic devices are applied in environmental and ecological research (“Smart and Green Nano-Tensiospectroscopy”), for drink analysis, for biosciences and biomedical applications, for forensic science.

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A crime scene can be where the crime was committed, or places where crime-related transportation, storage, and disposal acts had occurred. All locations where in there is the potential for the recovery of evidence must be handled in the same careful manner. It is usually achieved by cordoning off wide areas around the crime scenes so that evidences can be carefully recorded in great detail. To reduce the interferences with the normal way/pace of public life, it is often a dilemma in between "speeding up" the process and to "reduce" the risk of oversight. Modern computer and communication technologies have greatly facilitated crime scene data retrieval, management , and even the efficient manner of "on-line" analysis; for example, through harnessing the on-board GPS locating function, the camera (for image feature processing based operations), or the audio recorder (for acoustic based analysis). This project aims at exploring innovative approach to develop, utilising , and integrating mobile application programs (or mobile apps) to offer increased productivity on information processing considering limitations of mobile device and processor specification and configurations.

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This project aims to use a cognitive architecture (a computational theory of the human mind) to model human performance in a cognitively demanding task involving collaboration between multiple human and artificial agents. The model will specify the cognitive, perceptual and motor processes that underlie the performance of human operators as they supervise and direct the operation of a team of human and autonomous systems (e.g., pilots and UAVs) as they carry out a team mission. This involves the maintenance of situation awareness, integration and interpretation of (often conflicting and uncertain) information represented in diverse formats and from different sources, rapid situation assessment and decision making, and the appropriate communication of information and commands. The project will require the creation of a computer-based simulated task environment (a visual display for operators to monitor and direct the actions of the agents and actors). The environment will then be used to carry out controlled scenarios involving human participants to develop a cognitive task analysis of the activity under different experimental conditions (number of agents, degree of agent independence etc.). Finally, a cognitive model of the human operator interacting with the task environment will be developed using the ACT-R cognitive architecture.

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Analytics is an increasingly important area of research which has applications within organisations and society. Social media is one of the key areas influencing current societal trends. It is therefore very important to understand the impact of social media messaging on society and society on social media messaging. This form of analysis is also increasingly important for organisations, including businesses, that are looking at how they market and communicate with their stakeholders and customers. This research will therefore look at developing a more effective model of social media analytics enabling organisations and businesses to better understand the impact of their social media communications. Our published research work has the mathematical and computational implementation of how the eminent political scientist, Jürgen Habermas has characterized social and related trends, and developments; and in many application domains, again the mathematical and computational implementation due to eminent social scientist, Pierre Bourdieu, where a central role is played by homology in information spaces associated with, and defined by, our data sources.

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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 Internet of Things (IoT) envisions to connect billions of smart objects to the Internet, which can bring a promising future of smart cities. These objects are expected to generate large amounts of data, which should be exploited for further processing, especially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then completely sending all the captured data to the cloud is less useful. 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 this project, you are expected to explore novel computation models for applying the fog computing paradigm to IoT scenarios. In such scenarios, a set of edge computers are available and a significant proportion of these edge computers are mobile and can only provide opportunistic computation services to smart things which are also mobile and in need of computation services from the edge computers. Your main task is to build a theoretical model to capture the opportunistic computational resources available in the Fog so as to enhance data sharing in IoT.

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Supervised learning requires training data to optimise parameters in a given model - such as an artificial neural network. Often the data will describe a range of classes or groups and the model will be required to learn from the data to enable new values to be predicted from unseen samples (prediction networks) or be identified as belonging to a particular class of features (classification networks).

The generalisation capabilities of these models can depend on factors such as training data, network topology and training method. If some feature sets contain many more data samples than another's, bias can be introduced into the learning process because of the way in which the samples are presented to the network; this can cause difficulties in producing reliable models. Data reduction methods, to "even out" this effect, can lead to a loss of information and poor generalisation capabilities. Though it is possible to control the learning rate to some extent during training for some models, this is often arbitrarily done and is far from ideal.

This project will aim to answer four key questions

  1. Can the data be pre-processed/sorted effectively during each training epoch to allow order invariant classification to take place?
  2. Can the topology of the network be pre-constructed and/or modified during training so as to reduce sample bias and increase classification accuracy across all features?
  3. Can novel approximation estimators such as modified least squares methods and others, be used to asymptotically limit the effect of outlier bias on the training process?
  4. To what extent would any of the above affect current solution methods - such as gradient descent methods, back propagation etc., and how will this affect known convergence rates?

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It is widely acknowledged by UK Police that there are many data sources not currently processed during criminal investigations. Such data sources could have a significant role in speeding up a criminal investigation but are often ignored due to limitations on available investigative resources (e.g. available human capability). This project has a particular focus on investigating the relationship between location-specific information and the identification of criminal activity. Although the collection of location-specific information is contentious for privacy reasons, and is often prevented from being used even in criminal investigations, it may have significant potential in identifying unusual behaviour to quickly identify potential suspects. For example, behaviour studies have demonstrated that criminals often following similar traits, and such patterns might be identifiable through digital information. For example, it might be possible to identify unusual movement behaviour from processing mobile phone cell connection data. This project aims to investigate the feasibility of using location-specific data for the rapid detection and identification of unusual and potentially criminal behaviour. It is foreseen that this project will involve a thorough analysis of available data sources to identify a benchmark environment; developing algorithms to identify unusual behaviour in the location-specific data, and to perform a case study investigation to determine viability.

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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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Recent work has identified that there are significant security concerns over the introduction of Connected and Autonomous Vehicles (CAVs). This is often attributed to resulting from the way in which the CAV industry functions. For example, a lack of cyber-security requirements within the sub-contractor supply chain, as well as the desire to quickly deliver innovative functionality, can result in a lack of cyber-security consideration. The complexity of software running on embedded processing devices within CAVs has been identified as on par with some of the largest software projects currently in existence. It is highly likely that CAVs will be susceptible to cyber-attacks even if significant development resources are assigned. This is because they will remain an attractive target for criminals. This project aims to research into vulnerabilities that exist within the CAVs control systems, specifically focussing on the challenges of how a vehicle can self-determine if it has been compromised, how a ‘safe mode’ of operation can be maintained, and how the driver can assist the CAV in returning to full operation without any cyber-security or technical expert knowledge.

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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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Searching for parking in busy urban centres is frustrating for the driver. The challenge in finding parking space efficiently undoubtedly intensifies congestion on the roads, which further leads to increase in pollution from circling cars. This also results in a lot of wastage of fuel and investment and money. Smart parking in fog computing paradigms will enable efficient and near-real-time parking space allocation and finding, which can optimize the parking space effectively. Fog computing, which provides infrastructure for real-time data processing within the local physical areas of the application, has the potential to implement real-time way finding and parking space finding based on data gathered from parking sensors. In this project, you are expected to build smart parking models under the fog computing paradigms. Your main tasks include: (1) Construct smart parking application models for future smart cities using popular road traffic simulation tools, such as VISSIM, SimTraffic, etc.; (2) Develop fog computing solutions to smart parking in future smart cities using relevant network simulation tools, such as NetSim, OMNeT++, etc.; (3) Combine the constructed models inside road traffic simulation tools with the smart parking solutions developed in network simulation tools and evaluate the performance of the proposed smart parking system.

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Modern societies are highly dependent on complex, large-scale, software-intensive systems that increasingly operate within an environment of continuous availability, which is challenging to maintain and evolve in response to the inevitable changes in stakeholder goals and requirements of the system. Software architectures are the foundation of any software system and provide a mechanism for reasoning about core software quality requirements. While consensus on what sustainability means in the field of software engineering is still emerging, there has been a focus towards understanding technical sustainability whose overarching goal is for software developers to achieve maintainable and extendable systems. However, we lack consensus on how, and a set of tools to achieve sustainability in the design of software artifacts and systems, and how to quantify and measure software sustainability. The aim of this research is to design, develop, and integrate a software sustainability measurement framework for architectural-level reasoning.

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In the field of cyber security analysis, a large verity of different data sources are processed to extract knowledge to determine the state of the underlying security mechanisms, as well as establishing system use through available event logs. However, almost all services and software applications have a unique mechanism of logging events, which each follow a different format and contains different information. A challenge faced by many security practitioners is how to manually analyse such large volumes of diverse information to identify aspects key to monitoring and maintaining security provisions. Visualising the data in its standard text view is simply infeasible and there is a real need to provide illustrative aids, especially when considering the relationship between events in multiple log files. This research project will investigate the use of state-of-the-art visualisation techniques to process heterogeneous security logs to aid the human investigator in identifying key information of interest. It is foreseen that this project will involve a thorough analysis of event logs to identify key sources and benchmarking instances, the trial and error of different visualisation techniques, and the use of expert participants to validate any chosen approach.

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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.

Researcher Enviroment

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

This normally includes the submission of a research proposal. Read through the proposal guidelines 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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Important information

We will always try to deliver your course as described on this web page. However, sometimes we may have to make changes to aspects of a course or how it is delivered. We only make these changes if they are for reasons outside of our control, or where they are for our students' benefit. We will let you know about any such changes as soon as possible. Our regulations set out our procedure which we will follow when we need to make any such changes.

When you enrol as a student of the University, your study and time with us will be governed by a framework of regulations, policies and procedures, which form the basis of your agreement with us. These include regulations regarding the assessment of your course, academic integrity, your conduct (including attendance) and disciplinary procedure, fees and finance and compliance with visa requirements (where relevant). 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. lations, policies and procedures referred to.

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