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

6 January 2020

28 April 2020

Duration

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

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.

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 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 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

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 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

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 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

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

How to apply

Outline

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

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

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.

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 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.

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

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.

Research Enviroment

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 equips you with the 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 support as well as our own Programme.

We offer skills training through a programme designed to take advantage of technology platforms as well as face-to-face workshops and courses. The University has subscribed to Epigeum, a programme of on-line research training support designed and managed by staff at Imperial College London which will be accessed via 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 North West and Yorkshire PGR Training Group that allows PGRs to attend relevant training opportunities at other nearby universities.

Student support

Tuition fees

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Important information

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