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Computer Science and Informatics (MSc by Research)

2023-24 (also available for 2024-25, 2025-26)

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

Start date

2 October 2023

15 January 2024

15 April 2024

Duration

The maximum duration for an MSc by Research is 1 year (12 months) full-time or 2 years (24 months) part-time 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 September 2023

09 June 2023 for International and Scholarship Students

30 June 2023 for Home Students

For October 2023

09 June 2023 for International and Scholarship Students

30 June 2023 for Home Students

For January 2024

20 October 2023 for International and Scholarship Students

17 November 2023 for Home Students

For April 2024

26 January 2024 for International and Scholarship Students

23 February 2024 for Home Students

About the research degree

A Master of Science (MSc) by Research 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.

Our research degrees are available as full-time, part-time and some are offered distance learning.

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 develop your research skills by taking part in training courses and events.

You will be appointed a main supervisor who will normally be part of a supervisory team, comprising up to three members. The research supervisor will 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 of around 25,000 words, 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. 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 quest for sustainable urban development necessitates innovative approaches to assessing and enhancing energy performance at the neighbourhood level. This research proposes an AI-driven interactive visualization framework utilizing Network Graph Analysis (NGA) to compare energy performance across neighbourhoods. Traditional methods of energy performance analysis often lack the ability to dynamically visualize complex relationships and patterns among multiple entities. By integrating AI with NGA, this research aims to provide a sophisticated tool for visualizing and analyzing the interconnected energy performance metrics of residential buildings, facilitating more informed decision-making for policymakers, urban planners, and residents. This research seeks to bridge the gap between complex data analysis and practical application by providing a robust, AI-enhanced visualization tool that can drive substantial improvements in energy efficiency at the neighbourhood level.

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 development of co-simulation procedures has led to the development of sophisticated numerical dynamic analysis tools. These are able to couple two different simulations or more, running alongside each other. Such methods allow for the study of more complex systems by coupling different sub-systems or coupling different phenomena in the same system. The aim of this work involves the study and investigation of co-simulation methodologies and its application in numerical dynamic analysis tools. Different approaches are to be implemented and tested under a series or different case scenarios and benchmarks. The final objective of this work includes the development and implementation of a new co-simulation framework on a state-of-the-art Pantograph-catenary dynamic analysis tool. This is able to handle the numerical analyses of pantograph-catenary interaction, where the pantograph is modelled as a multibody system in interaction with finite element OLE model.

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

Pantograph-OLE Interaction plays a fundamental role in the traction of electric railway vehicles. The sliding contact between the overhead line and the pantograph contact strips must be as smooth as possible and uninterrupted. The study of this interaction under aerodynamic loads has become a key factor on the development of new overhead lines and current collection systems. The employment of numerical dynamic analyses tools to study pantograph-OLE interaction is now being accepted by the industry, as these types of software are becoming more reliable and accurate. Though, added model complexity is sought after in order that more complex problems can be analysed. One of these aspects is the inclusion of aerodynamic effects in these types of numerical studies, which today have an impact in the design of new pantographs and overhead line systems. This work aims to study the aerodynamic effects on the pantograph and the overhead line, and the development of a modelling methodology to include them in pantograph-OLE interaction numerical analyses. These methodologies are to be incorporated in a state-of-the-art dynamic analysis tool already developed, so its capabilities are augmented.

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

Sustainable Software Engineering has been defined as "the ability of software to be developed and used in a way that minimizes its negative impacts on the environment, economy, and society, while ensuring its long-term viability and quality". This project aims to analyse the impact of Aspect Oriented Programming (AOP), specifically the development of reusable aspects designed for efficiency and resilience, on Software Sustainability. This will involve the use of software metrics (including novel metrics developed for this purpose) to measure the impact of AOP on both the software development process and upon the sustainability of the end-product software system itself.

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

Argument mining is a research area within natural language processing. The aim of argument mining is the automatic identification and extraction of argumentative structure from real world textual resources such as legal documents, product reviews, online debates or newspaper articles. This project will involve a detailed research study of the structure of natural language arguments with the aim of devising new and effective computational argument mining techniques. The successful candidate will be expected to focus on an application domain and extend existing natural language or machine learning techniques applied to the argument mining domain.

Essential attributes for the candidate: • Experience of fundamental Natural Language Processing (NLP) or Machine Learning (ML) techniques. • Competent in applying NLP toolkits, such as NLTK or Spacy, or ML toolkits such as Scikit-Learn. • Knowledge of argumentation theory and defeasible reasoning would be advantageous. • Good written and communication skills. • Strong motivation, with evidence of independent research skills relevant to the project.

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

Argument mining is a research area within natural language processing. The aim of argument mining is the automatic identification and extraction of argumentative structure from real world textual resources such as legal documents, product reviews, online debates, or newspaper articles. This project will involve a detailed research study of argument mining in the context of legal decision-making which involves the extraction and analysis of arguments from legal texts, such as court cases, statutes, and legal opinions. The goal of the project is to identify the reasoning and evidence presented in legal documents to aid legal professionals in understanding, summarizing, and ultimately supporting them in decision-making processes. The underlying techniques will be argumentation theory, natural language processing (NLP), machine learning (ML) and large language models (LLMs).

Essential attributes for the candidate: • Experience of fundamental Natural Language Processing (NLP), Machine Learning (ML) techniques and Large Language Models (LLMs). • Knowledge of argumentation theory and defeasible reasoning would be advantageous. • Good written and communication skills. • Strong motivation, with evidence of independent research skills relevant to the project.

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

The ways in which we interact with computer systems is changing, with a vast array of alternative input methods now readily available to the 21st Century developer. Alongside these developments there is significant research into hand gesture recognition systems that provide the user with an efficient and effortless method of human-computer interaction. Typically, hand gestures are elicited from target users in Gesture Elicitation Studies (GES). This research project seeks to address two critical issues in traditional GES. Firstly, there is the potential for biases to be introduced by priming the participants before gestures are elicited. Secondly, the analysis of the gestures produced by the participants for higher level interactions is complicated. It is anticipated that this research will lead to a more rigorous framework for gesture analysis.

Key Objectives:

1) Biases Introduced by Priming: Investigate the potential biases introduced by traditional priming methods in GES. Develop strategies to minimise or account for any potential biases, to ensure more accurate and unbiased participant responses.

2) Gesture Analysis Challenges: Scrutinise the intricacies of analysing gestures produced in GES for immersive systems by considering factors such as speed, type, and scale. Develop a framework for the analysis of gestures to support the researcher addressing the challenge of distinguishing between different gestures.

3) Integration and Comparative Analysis: Integrate findings from the examination of biases and gesture analysis to generate a methodological framework suitable for immersive system GES. Conduct a comparative analysis between the traditional and refined methodologies, highlighting the advantages and limitations of each 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

It is increasingly important that the skills gained within learning are clearly mapped to job roles and that they are communicated clearly to employers, governments and educational providers. Using a 21st Century Skills framework this research will explore a range of courses and job roles to categorise learning and to therefore support learning analytics approaches based on digital skills development.

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 mobile devices and computer games controllers use small form factor vibrating disc motors to provide haptic feedback to the user. This technology enhances user experience and increases accessibility. Despite this widespread proliferation in consumer technologies, haptic feedback has not been harnessed to the same extent in creative computing applications such as music production and digital art production software. This research project seeks to explore the potential for enhancing the user experience within creative computing domains with haptic feedback.

Key Objectives:

1) Technology Transfer and Adaptation: Investigate the adaptability of small form factor vibrating disc motors from consumer devices to creative computing interfaces.

2) User Experience Enhancement: Design and implement haptic feedback mechanisms for creative computing tasks Evaluate the impact of haptic feedback on user experience and usability in these specific domains.

3) User-Centric Design Iterations: Engage with target users through iterative design sessions, to refine and optimize the haptic feedback features. Ensure the developed haptic feedback solution aligns with the user's requirements and preferences.

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

With the increasing success of deep learning models and the huge availability of social media data, many exciting research problems are open for exploration. In the quest to make computers more amenable to natural language through useful computational tools, the proposed project will focus on developing an NLP pipeline with a wide range of applications in uncovering meanings in data, especially those expressed in human natural language (with emphasis on low resource languages). Among the objectives of the project is to support pedagogical activities in low resource languages.

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 employment of finite element methods in engineering plays a large role in the analysis of structures. With the advancements in computer resources, dynamic analysis applications based on this method are able to analyse large and complex systems. This work aims on the development and implementation of novel finite element modelling methodologies, able to handle the dynamic interaction between the overhead line and the pantograph in railway systems. Focusing on the construction of finite element models of the overhead systems and its dynamic analysis. The newly developed modelling methods are to be incorporated in a state-of-the-art dynamic analysis tool already developed, so its capabilities are augmented. The new methodologies are to be validated using experimental line tests in collaboration with industrial partners of the railway sector.

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

The objective of this research is to leverage generative AI to enhance the assessment and prediction of Energy Performance Certificates (EPC) for residential buildings. Traditional methods of evaluating EPC ratings often rely on static data inputs and manual inspections, which can be time-consuming and prone to inaccuracies. By utilizing generative AI models, such as Deep Learning and Visual Analytics (Interactive Dashboards), this research aims to dynamically contextualize and interpret a wide range of data inputs, including historical energy consumption, architectural features, and real-time environmental conditions. This approach promises to provide more accurate and detailed assessments of a building's energy performance, offering actionable insights that can be tailored to specific contexts and user needs. The proposed study will develop and validate an AI-driven framework capable of generating comprehensive energy performance reports. These reports will not only predict EPC ratings with high precision but also offer contextual recommendations for energy efficiency improvements. The research will involve collecting extensive datasets from various residential buildings, training the AI models to understand the nuanced interactions between different factors affecting energy performance, and testing the system in real-world scenarios to ensure its reliability and practical applicability. By integrating generative AI for contextualization and prediction, this project aims to significantly improve the accuracy, efficiency, and usability of energy performance assessments, ultimately contributing to more sustainable housing practices and informed policy-making.

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

Hardware-In-the-Loop (HiL) is a novel simulation technique where a physical system interacts within a simulation in realtime. This technique is employed in the development and testing of complex systems. It allows mechanical systems to be tested, avoiding real tests which would otherwise be costly or unfeasible. There are challenges in setting up these types of simulation frameworks. The simulation program is required to be efficient and able to be evaluated in real-time. A robust control system is also necessary to acquire sensor data and control the response of all actuators accordingly. The development of this work is set on the development of a HiL framework for pantograph testing, in interaction with a numerical model of the overhead line. A fully equipped, world class, £ 3.5M pantograph test bench is available to procced with this works. Industrial partners in the transport and railway sector are to be involved in this work.

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 audio mixer in the core technology used to mix popular music. Over the last 50 years the audio mixer has evolved from physical, mainly hardware-based systems to virtual, mainly software-based systems that include greater visual feedback, precision, and processing capabilities. Despite this evolution the user interface of modern audio mixers still harks back to the original channel strip based analogue interfaces that emerged in the 1970s. Whilst this historical metaphorical reference arguably supports experienced users, it lacks relevance to newer/younger users. This presents a barrier to learnability. This research project seeks to bridge the historical design gap in audio mixing interfaces, making them more accessible and intuitive for a new generation of users while retaining functionality for experienced professionals.

Key Objectives:

1) Historical Interface Impact Analysis: Evaluate the impact of retaining the historical channel strip-based interface on the learnability of modern audio mixing systems. Examine how this design choice influences the user experience, particularly for newer and younger users.

2) User-Centric Redesign Strategies: Develop user-centric redesign strategies aimed at modernizing the audio mixing interface while preserving functionality. Integrate contemporary design elements that enhance learnability without compromising the efficiency enjoyed by experienced users.

3) Usability Testing and User Feedback: Conduct extensive usability testing of the redesigned interface with both experienced and novice users. Gather user feedback to refine the redesigned interface, ensuring it effectively addresses the learnability barrier for a diverse user demographic.

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

Multibody dynamics methods have established the grounds for advanced dynamic analysis applications, able to simulate mechanical systems. Multibody models are generally composed by a set of interconnected, rigid or flexible, bodies which undergo large translational and rotational displacements. Hence, large and complex mechanical systems are able to be analysed and studied in a computer-aided environment. The aim of this work involves the development and employment of multibody methodologies to produce realistic and accurate railway pantograph models. The pantograph is today a critical mechanical system in the operation of electric traction trains, both at conventional and hight speeds. The models developed are to be validated with experimental data obtained from line tests and/or test bench tests. The work here developed will allow to produce more accurate, realistic, and robust pantograph models, and better understand its mechanical behaviour when interacting the electrified overhead line.

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 anticipated that the number of individuals living with dementia in the United Kingdom will surpass 1.6 million by 2050, with an estimated 944,000 individuals affected as of 2024. The dementia statistics hub estimates that the economic impact is considerable, with costs estimated at $1.3 trillion in 2019 and a potential increase to $2.8 trillion by 2030. The complex nature of dementia, which encompasses a variety of subtypes such as Alzheimer's disease, vascular dementia, and Lewy body dementia, requires precise diagnostic methods that ensure the efficiency of treatment and care. Incorrect identification of genetic variations linked to dementia can lead to the annual expenditure of millions of pounds on diagnostic tests and treatments that are ineffective and do not address the underlying conditions. The multifaceted nature of the disease may not be fully captured by current diagnostic methods, which frequently rely on clinical assessments and neuroimaging. This proposal outlines a project aimed at developing predictive multi-modal models to classify dementia variants in at-risk older adults living in community settings using the frameworks and methodologies that we have previously published. This approach will integrate diverse data types that include gait patterns of the individuals as well as EEG signals along with demographic, genetic and lifestyle information to enhance the accuracy and early detection of dementia subtypes. Advanced Machine Learning techniques will be used to develop predictive models to distinguish different variants of dementia.

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

Overview: This research project focuses on music therapy in the digital era. Given recent advances in web-based technologies and the widespread proliferation of mobile computing devices, there exists the potential for powerful and affordable music therapy systems to be realised. Despite this potential, music therapists have yet to fully embrace technology in their practice (Magee & Burland, 2008). The primary focus of this research is to explore the needs of music therapists to generate a series of design guidelines that can be used to inform the creation of web-based music therapy systems.

Key Objectives:

1) User-Centred Exploration: Undertake interviews, surveys, and participatory design sessions to gain a thorough understanding of the perspectives and requirements of music therapists.

2) Theme-based Guideline Formulation: Analyse the outcomes of the user-centred exploration to identify recurring themes and patterns. From these themes, you will systematically develop a series of design guidelines that address the specific wants and needs of music therapists in the digital landscape. Simultaneously, you will identify avenues for future research.

3) Validation: Devise and evaluate a prototype music therapy system that is directly informed be the outcome of the first two objectives.

Magee, W. L., & Burland, K. (2008). Using Electronic Music Technologies in Music Therapy: Opportunities, Limitations and Clinical Indicators. British Journal of Music Therapy, 22(1), 3–15.

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

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 ensures you have the training and 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’s career support as well as our own programme. We also have a team dedicated to improving the academic English needed for research by our international PGRs.

Our training is delivered in a variety of ways to take advantage of technology platforms as well as face-to-face workshops and courses. The University subscribes to a programme of online research training support accessed through a dedicated researcher module in 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 Northwest and Yorkshire PGR Training Group that enables PGRs to attend relevant training opportunities at other nearby universities. We also hold a series of PGR focussed events such as 3 Minute Thesis, the PGR led research conference and regular informal events throughout the year.

Important information

We will always try to deliver your course as described on this web page. However, sometimes we may have to make changes as set out below.

When you are offered a place on a research degree, your offer will include confirmation of your supervisory team, and the topic you will be researching.

Whilst the University will use reasonable efforts to ensure your supervisory team remains the same, sometimes it may be necessary to make changes to your team for reasons outside the University’s control, for example if your supervisor leaves the University, or suffers from long term illness. Where this is the case, we will discuss these difficulties with you and seek to either put in place a new supervisory team, or help you to transfer to another research facility, in accordance with our Student Protection Plan.

Changes may also be necessary because of circumstances outside our reasonable control, for example the University being unable to access its buildings due to fire, flood or pandemic, or the University no longer being able to provide specialist equipment. Where this is the case, we will discuss these issues with you and agree any necessary changes.

Your research project is likely to evolve as you work on it and these minor changes are a natural and expected part of your study. However, we may need to make more significant changes to your topic of research during the course of your studies, either because your area of interest has changed, or because for reasons outside the University’s control we can no longer support your research. If this is the case, we will discuss any changes in topic with you and agree these in writing. If you are an international student, changing topics may affect your visa or ATAS clearance and if this is the case we will discuss this with you before any changes are agreed.

When you enrol as a student of the University, your study and time with us will be governed by the University’s Terms and Conditions and a framework of regulations, policies and procedures, which form the basis of your agreement with us. 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.