Computer Science and Informatics (PhD)

2020-21

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

Start date

21 September 2020

13 January 2021

26 April 2021

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.

Why choose Huddersfield?


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

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

What can I research?

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

Outline

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

Funding

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

Deadline

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

Supervisors

How to apply

Outline

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

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

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

Funding

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

Deadline

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

Supervisors

How to apply

Outline

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

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

Funding

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

Deadline

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

Supervisors

How to apply

Outline

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

Funding

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

Deadline

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

Supervisors

How to apply

Outline

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

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

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

Funding

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

Deadline

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

Supervisors

How to apply

Outline

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

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

Funding

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

Deadline

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

Supervisors

How to apply

Outline

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

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

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

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

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

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

Funding

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

Deadline

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

Supervisors

How to apply

Outline

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

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

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

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

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

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

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

Funding

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

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

Funding

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

Deadline

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

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.

Researcher Environment

The University of Huddersfield has a thriving research community made up of over 1,350 postgraduate research students. We have students studying on a part-time and full-time basis from all over the world with around 43% from overseas and 57% from the UK.

Research plays an important role in informing all our teaching and learning activities. Through undertaking research our staff remain up-to-date with the latest developments in their field, which means you develop knowledge and skills which are current and relevant to your specialist area.

[Find out more about our research staff and centres|http://www.hud.ac.uk/research/]

Student support

Tuition fees

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

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