Computer Science and Informatics (MSc by Research)

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.

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

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

• Dr Pedro Cabaco antunes
• Professor Joao Pombo

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.

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

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

• Dr Pedro Cabaco antunes
• Professor Joao Pombo

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.

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

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

• Dr Gary Allen
• Dr Simon Parkinson

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.

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

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

• Dr Quratul-ain Mahesar
• Dr Isa Inuwa-dutse

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.

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

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

• Dr Tianhua Chen
• Professor Grigoris Antoniou

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.

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

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

• Mr Christopher Dewey
• Dr Duke Gledhill

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.

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

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

• Professor Rupert Ward
• Professor Richard Hill

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with a wide range of symptoms and potential interventions. Information on ASD is scattered across diverse sources, including research literature, clinical guidelines, and therapeutic practices. Clinicians and researchers face the challenge of retrieving ASD-related information by manually analysing and combining this voluminous and heterogenous knowledge. This task can be time-consuming (in some cases even infeasible) and hinders decision making or the development of effective support strategies. This project aims to develop a comprehensive Autism Spectrum Disorder Knowledge Graph (ASD-KG) to streamline knowledge access and integration, empowering ASD research and clinical decision-making.

The development of a knowledge graph about autism spectrum disorder has the potential to significantly advance autism research and clinical practice. The proposed resource will aid in organising and interlinking medical knowledge about ASD, addressing the challenge of linking synonymous medical concepts from different sources. Utilising semantic search over ASD-KG it will promote efficient question answering, drastically reducing the time clinicians and researchers spend on manually analysing medical data sources. Furthermore, the ASD-KG will support complex queries that draw insights from multiple data sources, enhancing the clinical understanding of ASD.

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

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

• Dr Emmanouil Papadakis
• Dr Ilias Tachmazidis
• Dr Georgios Bargiannis

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.

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

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

• Dr Isa Inuwa-dutse
• Dr Sangeet Saha

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.

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

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

• Dr Pedro Cabaco antunes
• Professor Joao Pombo

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.

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

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

• Professor Grigoris Antoniou
• Dr Georgios Bargiannis

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.

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

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

• Dr Shamaila Iram
• Professor Rakesh Mishra

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.

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

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

• Dr Pedro Cabaco antunes
• Professor Joao Pombo

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.

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

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

• Mr Christopher Dewey
• Dr Austin Moore

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.

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

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

• Dr Tianhua Chen
• Professor Richard Hill

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.

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

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

• Professor Rupert Ward
• Professor Grigoris Antoniou
• Professor Richard Hill

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.

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

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

• Dr Pedro Cabaco antunes
• Professor Joao Pombo

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.

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

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

• Dr Pritesh Mistry
• Dr Shamaila Iram
• Dr Abirami Gunasekaran

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.

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

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

• Dr Gary Allen
• Dr Simon Parkinson

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.

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

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

• Professor Grigoris Antoniou
• Dr Ilias Tachmazidis

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.

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

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

• Mr Christopher Dewey
• Dr Duke Gledhill