Studentship Vacancies

PhD Studentship Vacancies

We currently have one vacancy for a fully funded PhD studentship based at The Open University in Milton Keynes starting on 1st October 2025. The successful applicant will receive a stipend on the level of a UKRI standard stipend (in 2024/25 this is £19,237 annually).

Applicants are required to develop a project proposal as part of the application process and should contact the named supervisor(s) for their topic of interest to get more information and guidance on developing their application. Visit our FAQs for more helpful information about our PhD studentships. Further details on the application process are available on the 'How to Apply' and 'Writing a PhD Proposal' pages.

The closing date for applications is: 09:00 (UK time) Monday, 3rd February 2025.

The following list of topics, grouped by research area, are available this year.

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Area 1. Blockchains and Decentralised Systems

A blockchain is an open, decentralised and trustable system. Without the need for any central control or mediator, blockchains allow us to rethink applications in a decentralised way, providing a provenance protocol for sharing data across disparate semi-trusting organisations.

• Decentralised, Trusted Explainable AI Reasoning

  Over recent times concerns have been raised on how companies sometimes abuse personal user data within their internal AI reasoning systems. What if we could decentralise the reasoning and put it directly in the hands of users? Can we create a framework where automated reasoning can be carried out without the need for a central authority? How can we facilitate trust in automated reasoning processes and explain this clearly to non-computer scientists? This research is important for the development of more transparent, trustworthy AI systems that can be trusted to make sometimes life-changing decisions in complex, dynamic environments.

  Supervisors: Dr Retno Larasati, Prof John Domingue and Dr Aisling Third

  Keywords: Knowledge Graphs Deep Learning Trust Blockchains Decentralised Ledgers

  Skillset: Programming (Web development, Distributed Ledgers) Software and web development Blockchain (e.g., Ethereum) Linked Data Knowledge Graphs Deep Learning

  Study mode: Full-time & Part-time

• Facilitating Trust in Collaborative AI and Human Ecosystems with Distributed Ledgers

  This PhD will focus on combining AI and blockchain technology to improve trust and cooperation between AI systems and humans in collaborative settings. The research aims to explore how distributed ledger technology can be used to enhance communication and coordination between AIs and humans, with the goal of creating trusted, efficient and effective collaborative ecosystems. The findings of this research could have important implications for a wide range of fields, including finance, supply chain management, and security. Ultimately, the goal is to provide a foundation to help AIs and humans work together more effectively, leading to better outcomes for all parties involved.

  Supervisors: Prof John Domingue, Dr Amel Bennaceur and Dr Aisling Third

  Keywords: Artificial Intelligence Autonomous Agents Trust Blockchains Decentralised Ledgers Linked Data Knowledge Graph

  Skillset: Software and web development Blockchain (e.g., Ethereum) Linked Data Autonomous Agents

  Study mode: Full-time & Part-time

• Intelligent Companions

  This PhD will focus on the technical challenges and potential applications of decentralised personalised individually-owned AI services running on personal devices. The candidate will explore questions, such as a) what the AI agents could learn with limited computational capabilities and user-specific data from individuals and households and b) how these agents would act as users' proxies and interact with available services and resources such as open knowledge bases and other centralised/specialised AI services. The candidate will develop new approaches to encoding long-term goals for conversational agents co-developed with their owner-users in real-life scenarios of lifelong wellbeing applications.

  Supervisors: Dr Alessio Antonini and Dr Iman Naja

  Keywords: Personal Computing Positive Computing Distributed Multi-Agent Systems Swarm Agents Data Ownership

  Skillset: Programming (mobile development, web development, graph databases) Multi-Agent Systems Conversational Agents (e.g., chatbots)

  Study mode: Full-time & Part-time

• Self-sovereign Valuable Personal Data

  The rise of Generative AI highlights the importance of, and issues with, Web data. Various communities - artists, authors, people from marginalised cultures, etc. - object to the use of their data for training AI, while tech companies claim anything publicly on the Web as fair game.

  Recent Web and Distributed Ledger technologies have promised a radically different model for data online, with concepts such as Decentralised Identifiers, Solid personal data pods, and machine-readable data licences.

  This PhD will investigate the social and technical possibilities and impacts of these developments for trust and equity on the Web, Generative AI, and its applications.

  Supervisors: Dr Aisling Third and Prof John Domingue

  Keywords: Identity Self-sovereignty Blockchain Decentralised Ledgers Linked Data Knowledge Graph Solid EDI

  Skillset: Software and web development Blockchain (e.g., Ethereum) Linked Data

  Study mode: Full-time & Part-time

Area 2. Computational Social Science

Computational Social Science research targets the enhancement of critical societal issues through the use of Artificial Intelligence solutions. This research aligns with the core values of The Open University, and has contributed to urgent and vital topics, such as misinformation detection, online radicalisation and extremism, crisis management, and climate change.

• Explainable AI for Neurodiverse Patients

  This PhD project focuses on advancing the realm of explainable AI by investigating the usefulness and accessibility of AI explanations to neurodiverse individuals, specifically patients. The research goal is to enhance the understanding and accessibility of AI decisions in the healthcare domain for this specific demographic. This PhD research will scrutinise existing AI algorithms to improve their explainability and adaptability to diverse cognitive styles within the neurodiverse spectrum. Simultaneously, it will investigate the human-computer interaction elements, aiming to create interfaces that neurodiverse individuals comprehend, trust, and engage with effectively. Through this interdisciplinary approach, the project seeks to contribute to the ethical and accessible development of AI, ensuring that the benefits of cutting-edge technologies are genuinely inclusive and empowering for all members of our diverse society.

  Supervisors: Dr Retno Larasati, Dr. Soraya Kouadri Mostefaoui and Dr Aisling Third

  Keywords: Explainable AI Artificial Intelligence for Equality, Diversity and Inclusion (AI4EDI) Accessible AI

  Skillset: Basics of Artificial Intelligence/Machine Learning (desired) Programming Qualitative Analysis Human-Computer Interaction Artificial Intelligence for Equality, Diversity and Inclusion (AI4EDI)

  Study mode: Full-time & Part-time

• Scaling Deliberative Democracy with AI-Augmented voting

  This PhD will explore transitioning large-scale online deliberation from open-ended discussions to structured collective decisions through novel AI-augmented deliberation mechanisms. It shall address key challenges in scaling deliberative democracy, including preference elicitation, opinion clustering, and consensus building. The research will base and extend on theoretical frameworks and synthesise with computational methods that combine natural language processing, social choice theory, and machine learning to transform argumentative discourse into meaningful collective choices while preserving democratic values of representation and inclusion. The research will advance theoretical frameworks for large-scale deliberative democracy while delivering practical tools for democratic innovation.

  Supervisors: Prof Anna De Liddo, Dr Lucas Anastasiou and Dr Tracie Farrell

  Keywords: Deliberative Democracy Collective Intelligence AI-Augmented Deliberation Collaborative and Social Computing NLP

  Skillset: Research Methods Interest in Social Choice Theory Data Science Software Development

  Study mode: Full-time & Part-time

Area 3. Data Science and Extended Intelligence

Data Science and Extended Intelligence go beyond efficient data infrastructure and engineering, it studies data empowered human processes that lead to smarter, fairer, more sustainable and equitable ways of living.

• Changing minds through Human-AI dialogues

  Set against recent Generative AI advancements, this PhD research will explore the dynamics of collaborative dialogue between humans and AI in online environments, guided by Bakhtin's dialogism, which emphasises divergence as well as convergence of viewpoints (Bakhtin, 1984; Trausan-Matu et al., 2021). The research will focus on its role in facilitating negotiation of meaning, group cognition, including countering misinformation and pseudoscience within these environments. The emphasis is on designing and evaluating innovative Human-AI collaborative frameworks. This PhD is ideal for those intrigued by AI, collaboration, and philosophy, and provides an opportunity to shape a developing area, merging technology and theory to augment learning processes in online communities such as wikis, social media websites, and citizen science platforms.

  Supervisors: Dr Nirwan Sharma, Prof Advaith Siddharthan and Dr Aisling Third

  Keywords: Collaborative Learning Human-Computer Interaction Dialogism Artificial Intelligence Citizen Science Machine Learning

  Skillset: Basic programming skills Interest in AI/Machine Learning Mixed methods research

  Study mode: Full-time & Part-time

• Data Integration with Knowledge Graphs and AI

  Integrated knowledge is key in industry domains such as smart cities, education, biomedicine, web science, and cultural heritage. However, transforming and encoding data remains a bottleneck, limiting the ability of large organisations to interpret vast, diverse datasets. The project aims at designing novel methods for building Knowledge Graphs (KG) integrating data from heterogeneous sources, combining symbolic (mappings, rules, plans) and subsymbolic AI such as machine learning and Large Language Models (LLM). The candidate will address issues in KG construction and application including (1) automating KG generation from structured and semi-structured data sources; (2) accessing non-RDF resources with SPARQL (Virtual Knowledge Graphs); (3) improving the usability of KG construction systems for non-expert users.

  Supervisors: Dr Enrico Daga and Dr Paul Mulholland

  Keywords: Data Integration Semantic Web

  Skillset: Solid Programming Skills Interest/Expertise in Data Science Artificial Intelligence Knowledge Graph technologies (RDF, SPARQL)

  Study mode: Full-time & Part-time

• Integrating Large Language Models and Knowledge Graphs for Advancing Scientific Discovery

  This PhD research focuses on merging the capabilities of Large Language Models (LLMs) and Knowledge Graphs (KGs) to support scientific research. These technologies can assist researchers, students, editors, and funding bodies in navigating scientific literature, exploring research trends, extracting relevant information, assessing research impact, and even formulating new scientific hypotheses. The student will integrate LLMs and KGs to facilitate scientific discovery and provide robust assistance to the scientific community. The project envisions developing novel approaches and innovative tools that streamline the research process and foster groundbreaking scientific discoveries.

  Supervisors: Dr Angelo Salatino, Dr Francesco Osborne and Prof Enrico Motta

  Keywords: Large Language Models Knowledge Graphs Scientometrics Scholarly Analytics Scholarly Data Semantic Web Science of Science

  Skillset: Interest/expertise in Science Data Science Computer Programming Natural Language Processing Data Integration

  Study mode: Full-time & Part-time

• Gene Regulatory Network in Prostate Cancer Drug Resistance

  This research will investigate the dynamic gene regulatory networks (GRNs) underlying prostate cancer progression to a drug-resistant phenotype. Employing RNA and ATAC sequencing on androgen-dependent, castration-resistant, and neuroendocrine prostate cancer cell colonies at multiple time points, this study will map the evolving landscape of gene expression and regulatory regions. The student will perform network analysis and leverage semantic web technologies (e.g., Gene Ontology) to identify key gene interactions and their temporal dynamics, illustrating the molecular mechanisms driving prostate cancer development. This research will contribute valuable insights into the complex interplay of genes in prostate cancer, with implications for improved diagnostics and treatment strategies.

  Supervisors: Dr Angelo Salatino, Prof Francesco Crea

  Keywords: Gene Ontology Clustering Gene Regulatory Networks

  Skillset: Science Biology Semantic Web Machine Learning Data Mining

  Study mode: Full-time & Part-time

Area 4. New Media in Society

New Media and Society research aims at going beyond the study of Computing and ICT from a technology perspective, and looks at improving our understating human values and the impact of technology innovations on people's lives and their communities. This research particularly looks at ways to use new media to promote social justice and tackle complex societal challenges of inclusion and disadvantage.

• Investigating Issues of Fairness and Balance in Media Coverage by Modelling the Dynamics of Topics, Viewpoints and Actors in the News

  This research will design AI techniques for modelling the news agenda at scale, focusing on understanding the dynamics of topics (what subjects are covered in the news), viewpoints (what perspectives are expressed for a given topic) and actors (who gets to express their perspective in the news). A key research challenge will be to develop effective AI techniques able to identify and visualise the dynamics of the various viewpoints emerging in the news at scale, providing insights on the life-cycle of topics and viewpoints and investigating issues of fairness and balance in news coverage. In the course of this project, the student will develop novel solutions based on machine learning, natural language processing techniques and knowledge graphs, taking advantage of the latest generation of Large Language Models.

  Supervisors: Prof Enrico Motta, Dr Enrico Daga, Dr Francesco Osborne and Dr Angelo Salatino

  Keywords: News Analytics Argument Extraction Viewpoint Extraction Topic Classification Data Science Deep Learning Knowledge Graphs Natural Language Processing Information Extraction Large Language Models

  Skillset: Programming Machine Learning Natural Language Processing Knowledge Graphs Interest/expertise in news and media

  Study mode: Full-time & Part-time

• Citizen Curation

  Citizen Curation can be defined as individuals and groups from outside the museum profession engaging in curatorial activities to communicate their own ideas, experiences, perspectives and stories. Increasingly, museums support visitors in developing and sharing their own interpretations of artworks. This can be particularly valuable for minoritized groups whose concerns and interests may not be reflected in the interpretations offered by the museum. There is great potential for new technology to support citizens in interpreting artworks and curating their own responses, for example, novel interfaces to support citizens in sharing and discussing their own interpretations or the use of AI technologies to assist in generating alternative interpretations of the same artwork. The candidate will develop and evaluate novel technology to assist in Citizen Curation.

  Supervisors: Dr Paul Mulholland and Dr Enrico Daga

  Keywords: Citizen Curation Museum Interpretation

  Skillset: Knowledge of HCI software development or AI Interest in museum engagement and new technologies

  Study mode: Full-time & Part-time

• Emerging Technologies in Higher Education

  The use of technology to enhance the learning experience is a vibrant research area transforming higher education. This PhD will investigate the potential of new and emerging technologies in education, for example exploring Artificial Intelligence (AI) solutions for facilitating personalised learning and self-regulated learning, supporting lifelong learners in their personal and professional progression, exploring the use of new forms of accreditation such as micro-credentials, etc.

  Supervisors: Dr Alexander Mikroyannidis and Dr Trevor Collins

  Keywords: Technology-Enhanced Learning Educational Technology Artificial Intelligence Personalised Learning Self-Regulated Learning Lifelong Learning Micro-Credentials

  Skillset: Education Skills/Interest Technology Skills/Interest Evaluation Studies Research Methods Written and Oral Communication

  Study mode: Full-time & Part-time

• The Role of Technology in Fieldwork Education

  Within the geosciences, biosciences and environmental sciences fieldwork is seen as a signature pedagogy that provides opportunities for students to put their learning into use, develops students' collaboration and team working skills, and fosters students' sense of belonging within their discipline. This PhD will investigate the role technology plays in fieldwork education. Depending on the applicants' interests and experience, the project may evaluate existing practice or develop and trial new applications. Potential areas of interest include: the use of technology to enhance in-field teaching; the affordances and limitations of virtual field experiences; and/or the use of web broadcasting and streaming technologies to provide remote access to field sites and field scientists.

  Supervisors: Dr Trevor Collins, Dr Sarah Davies and Dr Karen Kear

  Keywords: Educational Technology Experiential Learning Fieldwork Education Technology-Enhanced Learning

  Skillset: Evaluation studies Research Methods Technology development or application Written and oral communication

  Study mode: Full-time & Part-time

Area 5. Responsible Artificial Intelligence

This research addresses the potential risks associated with AI, such as bias, misinformation, as well as copyright and privacy violations, by advocating for principles like accountability, inclusivity, and human oversight. Within this area, we have a range of projects aiming to establish frameworks and best practices that align AI with societal values and balance innovation with ethical considerations.

• Responsible Use of AI in Recommender Systems for Finding Experts

  The objective of this project is to develop innovative AI methods for identifying experts possessing highly specific knowledge and skills. The process of finding experts with relevant skills is currently too resource intensive and prone to biases. The student will work with the world's largest and continuously growing dataset of full text open access research papers, hosted by the research group at core.ac.uk and which has over 30 million monthly active users. The student will be able to test the developed technology in production in a real-world use case with scientific publishers / funders.

  Supervisors: Dr David Pride and Prof Petr Knoth

  Keywords: Machine Learning Artificial Intelligence Big Data Open Science Open Access

  Skillset: Natural Language Processing Machine Learning Information Retrieval Data Mining Big Data

  Study mode: Full-time & Part-time

• Developing Neurosymbolic Architectures for Trustworthy Large Language Models

  This PhD research will explore advanced neurosymbolic methodologies to enhance the reliability and transparency of Large Language Models (LLMs) by integrating them with knowledge sources, including both textual data and knowledge graphs. It addresses significant limitations of current LLMs, such as biases, hallucinations, and challenges in processing infrequent data (the "long tail" problem). Key objectives include 1) evaluating various strategies for integrating LLMs with knowledge sources, and 2) developing hybrid architectures that combine these approaches. The ultimate aim is to build a new generation of verifiable and trustworthy LLMs, facilitating safe applications across critical domains such as business, science, and governance.

  Supervisors: Dr Francesco Osborne, Dr Angelo Salatino and Prof Enrico Motta

  Keywords: Large Language Models Knowledge Graphs Information Extraction Human-in-the-loop

  Skillset: Interest/expertise in Science Data Science Computer Programming Natural Language Processing Data Integration

  Study mode: Full-time & Part-time

• Towards an AI University

  Generative AI is set to reshape all sectors, including education, which plays a crucial role in preparing society to use this technology equitably and sustainably. This PhD aims to explore how Generative AI can promote high-quality education for all by envisioning an "AI University" where most functions are AI-supported. Key areas of focus include responsible AI in creating educational materials, teaching delivery, and assessment (both formative and summative). Emphasis will be placed on minimising bias in generated content and examining impacts on power dynamics, as well as the voices of students and human educators.

  Supervisors: Prof John Domingue, Dr Aisling Third and Dr Alexander Mikroyannidis

  Keywords: Generative AI Adult Learning University Education AI Assessment Social Good

  Skillset: Generative AI Large Language Models Knowledge Graphs Responsible AI Educational Technology

  Study mode: Full-time & Part-time

• Artificial Intelligence for Critical Social Justice

  This PhD project focuses on opportunities for using artificial intelligence to promote social justice, as viewed through a critical lens. Critical lenses may include decolonial theory, intersectional and feminist theory, queer theory, crip theory, or any other theoretical proposition that takes power into account when analysing how artificial intelligence could facilitate justice. The goal is to identify the types of social justice goals that are forwarded through critical theories, the motivations and barriers to this vision of justice, and places where artificial intelligence, its properties and capabilities could contribute toward these goals or to breaking down the barriers that exist. Through an interdisciplinary approach that includes technology, sociology and critical philosophy, the project seeks to contribute to responsible, ethical, impactful uses of AI.

  Supervisors: Dr Tracie Farrell, Dr Aisling Third and Dr Joseph Kwarteng

  Keywords: Artificial Intelligence Justice Critical Theory

  Skillset: Basics of Artificial Intelligence/Machine Learning (desired) Knowledge of critical theory (essential)

  Study mode: Full-time & Part-time

• Promoting Fairness in Education through and Responsible AI & technology development

  This research will explore how digital tools and data-driven interventions can reduce awarding gaps in education, specifically focusing on underrepresented or marginalised student groups. By examining how technology can support relevant Higher Education stakeholders, this project aims to design and evaluate scalable solutions that promote academic achievement and reduce performance disparities. The outcomes will contribute to inclusive pedagogical practices and offer insights into optimising educational equity using technological advancements.

  Supervisors: Dr Alba Morales-Tirado, Prof Miriam Fernandez and Dr Paul Mulholland

  Keywords: Higher Education Learning Analytics Responsible AI Awarding Gaps Social Good

  Skillset: Artificial Intelligence Data Science Software engineering or related fields Responsible AI Educational Technology

  Study mode: Full-time & Part-time

• Protecting Women Online: Responsible AI & technology for Gender-Based Safety

  This PhD project will investigate strategies to enhance the online safety of women, focusing on preventing digital harassment, stalking, and exploitation. It will assess current gaps in digital protections, analyse behaviours that create unsafe online spaces, and develop innovative solutions incorporating responsible AI and ethical digital practices. By blending sociological insights with advanced technological approaches, this research aims to reduce online gender-based violence and create safer, more empowering digital environments for women.

  This PhD will be done in collaboration with the Center for Protecting Women online. The student will benefit from the centre's expertise, network and collaborations with external partners

  Supervisors: Prof Miriam Fernandez and Dr Angel Pavon-Perez

  Keywords: Online Safety Women Responsible AI Human behaviour

  Skillset: Artificial Intelligence Data Science Software engineering or related fields Desired knowledge of Responsible AI Ethical Tech Gender Studies

  Study mode: Full-time & Part-time

• Understanding chronic illness through self-reported data: Causes, Symptoms and Treatments

  Chronic health conditions that are poorly understood due to a combination of factors such as discrimination, poor health care provision more generally, and attitudes toward acceptable adverse outcomes. The goal is to identify reliable sources of data where patients discuss their processes of self-diagnosis or make sense of years of misdiagnosis, and develop techniques for using this data to better inform and empower different stakeholders in managing health. Particularly, this project focuses on empowering patients to identify possible causes that can be investigated by a healthcare professional, explain their symptoms confidently, access whatever home care or self-management that may work for them, and consider the treatments available to them. Through an interdisciplinary approach that includes technology, sociology and health care studies, the project seeks to contribute a more ethical and impactful application of AI within the healthcare system.

  Supervisors: Dr Aisling Third, Dr Zhraa Alhaboby and Dr Tracie Farrell

  Keywords: Explainable AI Artificial Intelligence for Equality Diversity and Inclusion (AI4EDI) Accessible AI

  Skillset: Basics of Artificial Intelligence/Machine Learning Programming (desired) Qualitative Analysis Human-Computer Interaction Artificial Intelligence for Equality Diversity and Inclusion (AI4EDI)

  Study mode: Full-time & Part-time