Computer Science and Informatics (MSc by Research)

2019-20 (also available for 2020-21)

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

Start date

23 September 2019

6 January 2020

28 April 2020

Duration

The maximum duration for a full-time MSc by Research is 1 year (12 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.

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 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.

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

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 ongoing. 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

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

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 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 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

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.

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

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.

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

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.

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 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.

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

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.

Important information

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