Computer Science and Informatics (MSc by Research)

2021-22 (also available for 2020-21, 2022-23)

This course is eligible for Master's loan funding. Find out more.

Start date

1 October 2021

17 January 2022

25 April 2022

Duration

The maximum duration for a full-time MSc by Research is 1 year (12 months) or part-time is 2 years (24 months) with an optional submission pending (writing up period) of 4 months.

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

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

Application deadlines

For PGR start date September 2021

02 July 2021

About the research degree

A Master's by Research (MSc) allows you to undertake a one year (full-time) research degree. It contains little or no formal taught component. This type of study gives you the chance to explore a research topic over a shorter time than a more in-depth doctoral programme.

Research Master's students choose a specific project to work on and have a greater degree of independence in their work than is the case with a taught Master’s course.

You’ll be expected to work to an approved programme which you will develop in conjunction with your supervisor within the first few months of starting your studies.

Whilst undertaking the research project you will also have the opportunity to develop your research skills by taking part in training courses and events. The approved programme of training and research combines advanced study, research methodology and a substantial research project, or series of research projects in a chosen field.

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.

At the end of the project you write up your findings in the form of a short thesis not normally exceeding 25,000 words (excluding ancillary data), which will then be examined.

On successful completion, you will be awarded your degree and if you have enjoyed this taste of research you may then decide to apply for the full research doctoral degree (PhD).

Entry requirements

The normal entry requirements for enrolment on a MSc by Research is an upper second honours degree (2.1) from a UK university or a qualification of an equivalent standard, in a discipline appropriate to that of the proposed programme to be followed.

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

Why choose Huddersfield?


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

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

What can I research?

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

Outline

The 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 a thorough computer science education, and will have some specialized 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

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

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.

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

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

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

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

Funding

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

Deadline

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

Supervisors

How to apply

Outline

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

Funding

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

Deadline

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

Outline

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

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

Funding

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

Deadline

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

Supervisors

How to apply

Outline

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

Funding

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

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

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

Funding

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

Deadline

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.

Student support

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

Researcher Environment

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

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.

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