Royal Holloway Social Purpose CDT

Department of Strategy, International Business & Entrepreneurship

Dr Gül Berna Özcan

Political Economy, Geo-Politics of Business, Labour Migration, & Entrepreneurship

• Business-politics ties and firm growth
• Geopolitical dimension of internationalization of businesses
• Business models and transnational labour migration 
• Business lobbying, regional growth and welfare

Department of Strategy, International Business & Entrepreneurship

Dr Ioana Jipa-Muşat & Dr Gül Berna Özcan

Interdisciplinary study spanning Development Studies, International Political Economy, Critical Management Studies, Economic Geography and the study of Central and Eastern Europe

Dr Jipa-Muşat particularly welcomes prospective research students interested in the following (or related) themes:

• Labour regimes and global production networks/global value chains
• Divisions of labour, value capture, globalisation, and management
• The political economy of energy transitions in the global North and South
• Business politics and multinational corporations
• Histories of international political economy
• Histories of labour, production, and trade politics
• The transition to capitalism
• Workplace conflict, class composition, political organisation, and capitalist restructuring in post-war Europe
• Migration and development
• Neoliberalism and post-neoliberal policy agendas

Dr Jipa-Muşat is especially interested in applicants with backgrounds in Political Economy, Economic Geography, Development Studies, Critical Management/International Business, Political Sociology, or Economic History.

Prospective applicants are strongly encouraged to contact Dr Jipa-Muşat directly—with a CV and a brief research proposal—prior to submitting a formal application.

Department of Computer Science

Professor Li Zhang

Audio-Visual Mental Health Disorder Classification Using LLMs and Evolving Transformers

Millions of people globally experience depression, anxiety, or other mental health disorders. Mental health conditions such as depression often affect individuals’ communication styles (e.g. changes in tone, pitch, pace, pauses, energy, and word choice) and behaviour patterns (e.g. social withdrawal, loss of motivation, fatigue, slowed movements, irritability, disturbed sleep or appetite). Early detection and intervention can significantly improve treatment outcomes, but the lack of an objective and effective detection method remains a major barrier.

To tackle the above issues, this project will develop novel deep learning algorithms, LLMs, and audio/vision transformers with various attention mechanisms, to identify depression, anxiety and early warnings of other mental health conditions using audio-visual inputs. Weakly supervised and zero/few-shot learning methods will also be exploited to classify unseen conditions without or with limited training samples, in order to tackle data sparsity issues. The proposed models will be comprehensively evaluated using existing public audio-visual depression, mental health disorder, and emotion datasets to ascertain their effectiveness.

Applicants should have the equivalent of a BSc (first-class) or MSc (distinction) degree from Computer Science/Engineering with research expertise in machine learning and deep learning. Applicants should have proficient programming skills in Python, MATLAB, and C++/Java. He/she should have good oral communication and academic writing skills. Relevant publication records would be advantageous. The selected candidate will be working with researchers from both Computer Science and Biological Sciences, with research support from DDM Health Ltd.

Applicants are encouraged to send their CVs, abstract and publications to Professor Li Zhang (li.zhang@rhul.ac.uk) for an informal discussion before completing online applications. Formal applications must be submitted through the University online application system after discussions with the supervisor.

Department of Computer Science

Dr Santiago Franco Aixela

AI-Powered Cooperative Drone Navigation in Challenging Environments

This PhD project explores how safe and reliable drone operations can be achieved through the integration of explainable AI with realistic simulation, addressing both technological and societal challenges. The work begins with GPS-denial scenarios and extends to centralised navigation and localisation, which are vital for the commercialisation of drones. As regulators emphasise centralised control over 5G networks for next-generation delivery drones, ensuring reliability and safety is essential.

A high-fidelity simulation environment will be created using frameworks such as ArduPilot and ROS2 to model UAV hardware and multi-drone coordination. Beyond serving as an experimental testbed, this simulator will provide trustworthy data for regulators, insurers, and policymakers. Validation through the Omnidrome facility will ensure that simulated behaviours align with real-world UAV platforms, strengthening the credibility of results.

Research directions include: developing explainable decision-making frameworks for urban drone operations; investigating swarm coordination with attention to fairness, prioritisation, and transparency; comparing centralised and decentralised control for BVLOS drones; and exploring air traffic management concepts such as U-space/UTM grids and their regulatory implications.

Key outcomes will include safety-first frameworks for cooperative drone operations, contributions to insurance and regulatory design through reliable data, and the advancement of explainable swarm AI that balances efficiency with fairness and accountability. Crucially, the project will inform education and public information campaigns to ease the integration of drones into shared spaces, from delivery to emergency services. By connecting technical innovation with regulatory and societal needs, this PhD will accelerate the safe and trusted adoption of UAV technologies.

Department of Computer Science

Dr David Tena Cucala

Accelerating Drug Discovery with Interpretable Machine Learning

The discovery of new drugs is a slow and costly process, often hindered by the complexity of predicting both therapeutic effects and potential toxicity. Machine Learning (ML) has shown promise in accelerating this process by suggesting candidate molecules; however, traditional ML models are largely black boxes, offering limited scientific insight beyond 'yes-no' predictions.

Researchers at Royal Holloway have developed a novel set of ML techniques, based on interpretable graph neural networks, that combine predictive power with strong interpretability, enabling the extraction of formal theories from neural networks. This approach allows not only accurate predictions of drug efficacy and toxicity but also scientifically meaningful explanations, facilitating safer and more informed drug design, and guiding the search and discovery of new drugs.

This project will proceed in two phases. Phase One will focus on existing medicinal benchmarks, where we will train and fine-tune explainable graph neural networks to predict toxicity and therapeutic effects accurately. A key objective is to ensure that the models’ explanations are correct, consistent, and scientifically interpretable. Phase Two will extend this work to real-world applications in collaboration with industry partners. We will apply our models to novel datasets and ongoing research in molecule suggestion, aiming to identify promising drug candidates while maintaining rigorous interpretability and safety standards.

By integrating cutting-edge explainable ML with practical drug discovery pipelines, this project seeks to accelerate the identification of effective, low-toxicity drugs, bridging the gap between computational prediction and actionable scientific insight.

Department of Computer Science

Professor Hugh Shanahan

Open Science

Research data and software are integral to nearly all areas of academic research. Research infrastructures are nonetheless vulnerable. They are susceptible to a wide variety of different risks such as financial sustainability, natural disasters and civil upheaval. Indeed the issue of risk is so complicated that there are arguments to see it as a social construct rather than something that can be expressed as a probabilistic risk.

Larger repositories have extensive plans, documentation and certification processes to address these risks. Smaller repositories or those in Low and Middle Income Countries, do not have sufficient resources to carry this out. This project will consider methods on how to address this question in this low-resourced context. Solutions here will also have an application for at risk digital services in poorer regions of the UK.
This project will consider two approaches, namely mirroring of repositories and lightweight methods to encapsulate repositories for data rescue.

The project will work in three phases.

Phase I - understand the breadth of the problem. During this part of the project the research will undertake to understand how many repositories do not have a sufficient risk plan in place and where these can be found. A survey will be sent out to relevant repositories to determine their attitudes towards this issue.

Phase II - Outline solutions. During this phase scoping will be carried out to determine what type of solutions are optimal given the priorities of those surveyed during the phase I.

Phase III - Implementation. In this final phase implementations for mirroring repositories (including facilitation processes) and lightweight encapsulation will be carried out.

This project will suit applicants with a software development background and a strong interest in Epistemic (knowledge) justice and/or Open Science. A number of different possible partners with expertise in repositories are being approached.

Department of Computer Science

Professor Kostas Stathis

Verifiable Agentic AI for Accessible Use of Digital Devices

The rapid rise of large language model (LLM)–based and vision–language model (VLM)–based agents offers unprecedented opportunities to improve the accessibility of digital devices for people with disabilities. These agentic systems, capable of interpreting natural language, understanding visual context, and autonomously carrying out tasks, promise to transform how users interact with computers, tablets, and smartphones. Tasks that once required specialised assistive technologies may soon be achieved through flexible, general-purpose AI agents, opening new possibilities for independence and inclusion.

However, these advances come with serious challenges. Current LLM- and VLM-based systems are inherently unreliable: they may produce errors, misinterpret intent, or hallucinate information. For users with disabilities, such failures can introduce significant risks, ranging from simple frustration to potential harm. In addition, these agents often require access to sensitive personal data to function effectively, raising concerns about privacy, security, and user trust. Without robust verification mechanisms and safeguards, their potential cannot be fully realised in accessibility contexts.

This PhD project invites investigation into how agentic AI systems can be made verifiable, reliable, and safe for accessible use of digital devices. Key research questions include: How can we design methods to formally verify the behaviour of LLM- and VLM-based agents? What approaches can ensure privacy-preserving use of sensitive data while maintaining functionality? How can verification techniques be integrated into user-facing systems without compromising usability for individuals with disabilities?
The project sits at the intersection of AI safety, accessibility, and human–computer interaction. It is well-suited to candidates interested in exploring technical, theoretical, and ethical aspects of agentic AI. We welcome applicants eager to address these broad questions and also encourage inquiries into related areas, such as secure AI deployment, adaptive assistive technologies, and trustworthy multimodal systems.

Department of Computer Science

Dr Nicolo Colombo

Trustworthy fairness measures for group-unbiased AI

AI has applications in critical and life-changing domains, as health, law, and welfare. For example, it helps predict cancer from blood samples, unlock phones through face recognition, or estimate solvency risks from bank transactions. Most AI systems are based on machine learning algorithms, which exploit historical data to predict future or non-observable events. By processing individual and sensitive information, driving personal behaviours and influencing policymakers, such learning machines have been shaping our world over the past few years, with unpredictable consequences in the near future.

Trustworthiness, transparency, and fairness have become essential requirements for any AI-based system. Central to all is the notion of algorithmic bias, which refers to systems that disproportionately disadvantage particular groups of people. Often caused by unbalanced or low-quality training data, algorithmic bias produces latent and systemic discrimination patterns, in particular when unfair outputs are used in decision-making. Urgent questions arise. When do machine learning models give unequal predictions? Can we detect accuracy disparities to avoid discrimination?

The problem is nontrivial. Affected subpopulations are often associated with protected characteristics, such as age, disability, gender, or race (as defined by the Equality Act 2010 in the UK), making inhomogeneities and unfairness invisible or hard to identify. Ignoring the problem and deploying biased models is not only unethical but could also be illegal. Existing fairness criteria, on the other hand, are usually based on empirical estimations and become unreliable for small population sizes.

In this project, you will leverage Conformal Prediction, an increasingly popular uncertainty-quantification strategy, to assess the reliability of bias metrics. By developing group-adaptive versions of the standard conformal algorithm, you will produce confidence levels that remain valid at the stratified level, ensure they are compatible with sample-based existing measures of fairness, and measure the effects of the estimate uncertainty on AI-driven decision-making systems.

Department of Electronic Engineering

Dr Beenish Ayaz

Sustainable Underwater Wireless Sensor Networks for Subsea AI Data Centres and Marine Environmental Monitoring

The world's oceans face unprecedented threats from climate change, pollution, overfishing, and habitat destruction, with profound implications for ecological stability and human communities that depend on marine resources. Simultaneously, the exponential growth of artificial intelligence (AI) has created an insatiable demand for computing power, leading to the emergence of underwater data centres that leverage natural seawater cooling for improved energy efficiency. China's recent deployment of the world's first operational underwater AI data centre off the coast of Sanya represents a significant milestone in subsea computing infrastructure. These facilities offer potential solutions to energy challenges but also raise pressing questions around environmental impact and social responsibility.

Underwater Wireless Sensor Networks (UWSNs) is a transformative technology for monitoring and protecting marine environments, consisting of distributed sensors that collect and transmit data through aqueous environments. These networks enable real-time observation of ocean parameters, wildlife tracking, pollution detection, and early warning systems for natural disasters. However, current UWSN technologies face significant limitations including energy efficiency challenges, communication reliability issues, high deployment costs, and limited accessibility for communities who would benefit most from marine data.

This project will address these challenges by advancing UWSN technologies to serve both environmental and societal purposes aligned with Royal Holloway’s mission as a University of Social Purpose. The key question it will address is how to optimise multi-modal UWSN architectures to monitor data centre environmental impact while balancing data rate, energy consumption, and reliability in harsh underwater conditions. Through technical innovation and social responsibility, the project will advance sustainable digital infrastructure while protecting marine ecosystems and supporting inclusive global futures.

Applicants and stakeholders interested in working in this area or a related research topic are encouraged to get in touch with Beenish.Ayaz@rhul.ac.uk

Department of Electronic Engineering

Dr Vladimir Dyo

Robust Communication for Autonomous Drone Swarms

Drone systems used in search-and-rescue and disaster relief missions often operate in areas without communication infrastructure and therefore rely on centralised ground-to-drone links, a model that fundamentally limits operating range, reliability, and ultimately the mission success. While mesh networking has emerged as an approach to overcome some of these constraints, its performance degrades significantly in highly dynamic topologies, undermining the reliability required for mission-critical communications.

The goal of the proposed PhD project is to address these limitations by applying network coding algorithms, such as fountain coding, to ensure robust message delivery in the presence of intermittent links, interference, and highly dynamic topologies. The rateless property of network coding techniques will be exploited to eliminate the need for packet retransmissions and acknowledgements, reducing the network overhead and improving message delivery rates. The project will also investigate the trade-offs among communication, onboard AI processing costs, and perception fidelity, with the aim of reducing energy consumption and latency.

The project will proceed in three phases. First, a swarm networking protocol will be developed and evaluated through network simulation based on real mobility traces and drone mobility models. Second, the proposed approaches will be prototyped and tested within the OmniDrome facility. Finally, the solutions will be implemented and validated in real-world outdoor settings in collaboration with an industry partner.
The project therefore provides an opportunity to contribute to both fundamental knowledge and practical solutions.

We welcome applicants with strong backgrounds in embedded systems, networking / communications engineering, or related disciplines. For informal discussion or questions, please get in touch with Dr Vladimir Dyo (vladimir.dyo@rhul.ac.uk) before submitting a formal proposal.

Department of Information Security

Dr Christian Weinert & Dr Maryam Mehrnezhad

Privacy-Enhancing Technologies (PETs) for Social Good

Are you driven by the potential of advanced technology to address societal challenges? This PhD project, “PETs for Social Good”, offers an exciting opportunity to explore the development of privacy-preserving technologies (PETs) for processing sensitive data with a focus on enabling positive societal impact. Inspired by initiatives such as the Boston Women’s Workforce Council’s (BWWC) use of Secure Multi-Party Computation (SMPC) to measure gender and racial wage gaps, this project aims to design and implement PETs-based solutions that protect the data of marginalized and at-risk groups while fulfilling a broader social purpose.

You will tackle the technical and non-technical challenges in developing PETs that can be applied in sensitive contexts. Your research will focus on ensuring that these technologies allow organizations to extract meaningful insights without compromising the privacy (as well as security and safety) of vulnerable populations, and by extension all citizens. Key questions include: How can PETs be adapted to meet the specific needs of at-risk groups? What are the technological and practical trade-offs when ensuring both user and data privacy as well as system security and usability?

This project offers interdisciplinary collaboration opportunities, drawing on expertise in cryptography, computer and social sciences. The research will involve potential case studies in fields such as income inequality, healthcare disparities, and workforce diversity, contributing to a growing effort to use technology for social good while maintaining robust privacy protections.

Depending on the background of the successful applicant, the deliverables of the project can be cryptographic protocol designs and attacks, proof-of-concept implementations (e.g., privacy dashboards), and participatory risk modelling frameworks. We look forward to discussing the project in more detail with interested applicants.

Applicants are requested to get in touch with the supervisory team (Christian.Weinert@rhul.ac.uk and Maryam.Mehrnezhad@rhul.ac.uk) for informal discussions before submitting a formal application to the University.

Department of Information Security

Dr Maryam Mehrnezhad & Dr Christian Weinert

Digital Safety for Victims of Technology-enabled Domestic Abuse

This PhD project offers an exciting opportunity to develop transformative support for survivors of technology-enabled domestic abuse. You will address the urgent challenge of digital threats, such as smart home surveillance and online harassment, which are increasingly weaponized in abusive relationships. The project will mainly focus on two aspects:

1. Tech Abuse Forensics: Through collaborative work, you will establish a framework for case workers to safely and ethically assist survivors with identifying and mitigating tech abuse. In particular, you will develop a comprehensive protocol for evaluating a victim’s home environment and electronic devices, identifying and recording signs of tech abuse, and guiding them through a process towards digital safety.

2. Tech Abuse Chatbot: By creating a secure and accessible AI-driven chatbot, you will provide guidance for survivors, offering crucial support that is often unavailable through traditional, overstretched resources. The core of this work is to apply cutting-edge AI technology within a framework built on privacy-by-design principles to ensure safety and data protection for vulnerable users.

This project will be conducted in partnership with a domestic abuse charity, guaranteeing it to be grounded in real-world needs. The development process will be deeply collaborative, following a participatory design methodology. This approach ensures that the frameworks and tools will not only be technologically sound but also user-centred.

We offer a unique opportunity to apply advanced computer science, information security, and AI to a critical social issue, creating scalable and empowering tools for the public good. You will not only contribute to a safer digital future for domestic abuse survivors but also establish a new standard for ethical tech development in sensitive social contexts.

Applicants are requested to get in touch with the supervisory team (Maryam.Mehrnezhad@rhul.ac.uk and Christian.Weinert@rhul.ac.uk) for informal discussions before submitting a formal application to the University.

Department of Physics

Professor John Saunders & Professor Andrew Casey

Advances in ultralow temperature cryogenic techniques and sensors

Advances in low temperature cryogenic technology underpin both fundamental scientific discovery and the developing quantum economy. The global market for dilution refrigerators is experiencing rapid current and projected growth due to their importance in quantum computing and scientific research at the low temperature frontier. This project will seek to make advances in how we cool quantum materials and quantum sensors to the lowest temperatures, and measure those temperatures, to deliver lower noise, improved performance and sensitivity. The three pillars of the project are: to understand the conditions for anomalously low thermal boundary resistance between liquid helium and solids; to fully characterise the thermodynamic properties of PrNi5, a workhorse material for cooling to sub-mK temperatures; to develop simplified superconducting electronics to read-out noise thermometers, destined to be the thermometer of choice over four decades in temperature below 1 K. This work will stand on a set of unique experimental capabilities developed at the world-renowned London Low Temperature Laboratory https://lltl.uk/. Our intention is that this project will be in collaboration with Oxford Instruments Nanoscience https://nanoscience.oxinst.com/ within the umbrella of Quantum Design.

Department of Biological Sciences

Dr Tina Steinbrecher

Seeds for the Future: Understanding and improving seed resilience to support sustainable food and biodiversity under climate change

Seeds and Climate Resilience: Unlocking Germination for Biodiversity and Food Security

Seeds are at the heart of both biodiversity and agriculture, yet their ability to germinate and establish seedlings is highly vulnerable to environmental stresses such as heat and drought. Climate change threatens not only crop productivity but also the survival of wild plant species essential for resilient ecosystems. This project will investigate how seeds respond to temperature and related environmental factors, with the aim of developing knowledge and technologies that support both food security and biodiversity conservation.

Key research questions include:

-How do temperature and stress conditions shape seed germination and seedling establishment?

-What traits enable some seeds to withstand environmental variability?

-Can seed technologies, such as priming, enhance resilience and performance under climate stress?

The studentship will be co-supervised with the Millennium Seed Bank at the Royal Botanic Gardens, Kew, offering unique access to expertise and seed collections. The collaboration will link fundamental biology to applied outcomes, from conservation strategies for threatened species to sustainable approaches for crop production.

In line with RHUL’s Social Purpose mission, the project seeks to generate knowledge that directly addresses pressing societal needs: ensuring reliable food systems, supporting climate adaptation, and protecting biodiversity. By bridging fundamental plant science with practical applications, the work has the potential to impact ecological restoration, sustainable agriculture, and long-term community resilience.

We welcome applicants with interests in seed biology, plant ecology, or applied plant science, and are open to shaping the project around related themes such as seed dormancy, longevity, or novel seed enhancement strategies.

Department of Biological Sciences

Dr Cristina Garcia

Bias Unmasked: Safeguarding Scientific Integrity in the Age of AI

|| SUMMARY || Science stands at a pivotal moment: while generative AI promises to transform research by accelerating data analysis, automating writing, and revealing new connections, these same tools may quietly entrench the cognitive biases that already threaten the credibility of science. Gender disparities, cultural blind spots, and other systemic biases have long distorted research findings. When AI systems—trained on historically biased datasets—become embedded in discovery and decision making (policy), those distortions risk becoming magnified and harder to detect, with far-reaching consequences for evidence-based decision-making.

|| AIMS || This PhD aims to critically investigate how generative AI shapes, amplifies, or suppresses cognitive biases across multiple scientific disciplines. Using an innovative blend of computational analysis and controlled experiments, the research will identify which biases are most vulnerable to AI amplification and map their expression across diverse research domains. This dual approach will not only expose hidden distortions but also test new methods for detecting and mitigating bias.

|| OUTCOMES || The impact will be transformative. By revealing how AI influences scientific thinking and findings, this project will inform the design of bias-aware AI systems and develop robust, evidence-based guidelines for their responsible use. The PhD student will co-design pioneering experimental protocols and statistical frameworks that actively counteract AI-driven biases. In doing so, the research will safeguard scientific integrity at a time when trust in evidence is under intense scrutiny. Its outcomes will set new standards for AI-assisted research, protecting credibility for future generations and ensuring that artificial intelligence becomes a force for greater fairness, inclusivity, and reliability in scientific discovery.

Department of Earth Sciences

Dr Jonathan Paul

Flood risk management and mitigation: Leveraging local knowledge to produce actionable data and meaningful protective strategies

The January 2024 floods of the Thames Valley (west London to Oxford) were unprecedented in their sweeping scale and severity. Floodplains are supposed to flood: yet the inexorable need to build here has increased flood risk, which compounds existing longer-term climate trends that see dramatic, intense cloudbursts dominating in a region that is actually drying out.

Surprisingly, the provenance of most floodwater is the ground – slow, vertically upward seepages from a permeable gravel substrate. Yet knowledge of the disposition of this groundwater is as poor as its regulation and governance, which falls to local councils (rather than an expert body like the Environment Agency/EA).

The purpose of this studentship is to co-develop, with local people, strategies of mapping this flooding, and to do something about it. The candidate will work with residents’ associations across the Thames Valley, as well as local government and the EA, to capture local knowledge of historical flooding and transform this qualitative information into data for hydrological modelling. Field surveys and laboratory analyses will inform analytical and numerical models to elucidate water flow direction and speeds in the ground, and predictions of flood risk. We will use elements of supervised learning – based on flood maps co-developed with the local community – to validate the modelling approach. A proof-of-concept of this methodology has been applied to Staines; see https://onlinelibrary.wiley.com/doi/10.1111/jfr3.70050 (2025).

Social purpose fit-to-call:
• Flood risk reduction and resilience building in the immediate area around RHUL
• Knowledge co-creation with local people throughout the research project
• Raising environmental awareness of local communities (often marginalised groups)
• Development of new local research partnerships for RHUL
• Influence on policy change regarding water resource management first at local, then national, level, also affecting planning decisions e.g. floodplain construction

Department of Earth Sciences and Geography

Professor Prof Jürgen Adam, Dr Jonathan Paul & Dr Adrian Palmer 

Protecting the Past, Preparing for the Future: UAVs for Climate-Resilient Gardens

Integrating UAV, Environmental, and Citizen Science Data to Support Climate Change Resilience Strategies in National Trust Parks and Gardens.

This PhD offers an exciting opportunity to pioneer cutting-edge methods that harness drones, robotics, and geospatial analysis to protect the UK’s unique heritage gardens and parks from the growing impacts of climate change. In collaboration with the Omnidrome Research & Innovation Centre and the National Trust, you will develop scalable strategies for monitoring and managing these iconic landscapes, balancing ecological health with historic and cultural values.

Heritage parks and gardens are increasingly vulnerable to climate-driven stresses such as flooding, drought, erosion, and ecological shifts. Effective management requires timely and high-resolution data to understand changing geomorphological, hydrological, and environmental conditions. UAVs—both aerial and surface (and submersibles for water systems)—equipped with diverse sensors, make it possible to collect continuous, scalable time-series data. Integrating these datasets with on-the-ground operational records and community science observations offers a transformative pathway to develop resilient management strategies that are both data-driven and community informed.

You will:

• Deploy UAVs and ROVs to conduct repeated multi-sensor surveys of selected National Trust parks and gardens.
• Integrate remote sensing, environmental monitoring, operational, and community science data using advanced geospatial, statistical, and AI/ML methods.
• Analyse time-series data to identify climate-driven trends and critical thresholds.
• Co-develop scalable, transferable monitoring and planning strategies with National Trust managers to inform practical climate resilience policies. 

The project is supported by our Omnidrome research centre. You will gain expertise in drone and ROV operations, advanced geospatial analysis, AI/ML techniques, and stakeholder engagement. Working at the interface of technology, environmental science, and cultural heritage, this project makes a direct contribution to protecting some of the UK’s most treasured landscapes.

We welcome applications from motivated candidates eager to combine technical innovation with applied environmental impact. Fieldwork, geospatial, or data science experience is beneficial but not essential—full training will be provided.

Department of Health Studies

Dr Fabrizia Ratto

Exploring interdependence between pollinators, medicinal plants, and disease prevention and treatment

Most of the global population across different geographical regions and socioeconomic contexts, and particularly in the Global South, relies on traditional remedies for their healthcare and nutrition. This reliance is rooted in cultural traditions and tightly linked to local biodiversity.

Despite their value, most medicinal plants are endangered, due to over-harvesting from the wild, this is compounded with other stressors including climate change and environmental degradation. The majority are flowering plants that likely depend on animal pollination for reproduction. Thus, the worldwide decline of pollinators may exacerbate the loss of medicinal plants, threatening ecosystem functioning and access to these resources for human health. Furthermore, with many drug compounds producing adverse effects—particularly for people with multiple chronic conditions—there is an urgent need to identify safer alternatives. Our previous work has shown that plant-derived compounds, such as Metformin, are beneficial against cardiovascular disease with few side effects. Yet many medicinal plant species remain unexplored for their potential benefits in treating and preventing cardiovascular disease as well as for their long-term resilience to natural systems degradation.

In partnership with Royal Botanical Gardens, Kew, this interdisciplinary PhD will investigate the ecological resilience and the therapeutic efficacy of medicinal plants with potential to reduce cardiovascular inflammation. The methods will build on previous literature and ethnobotanical reviews to identify up to five widely used medicinal plants with relevant therapeutic properties. Ecological bioassays and pollination experiments will be combined with analyses of environmental and cultural threats to plant survival, such as over-harvesting and climate change. Laboratory analysis will involve identifying bioactive compounds in these species, assessing their pharmacological effects using cell culture and molecular biology techniques.

Understanding these dynamics will have profound social impact, offering cost-effective solutions for communities that rely on medicinal plants for traditional healthcare and livelihoods and generating knowledge to address the adverse impacts of human activities on biodiversity.

Department of Health Studies

Dr Anusha Seneviratne

Breathe London: Community engagement, public health, and the pursuit of “clean air”

The proposed PhD project “Breathe London” aims to address urgent challenges related to poor air quality in London and its neighbouring regions. Framed as a public health emergency—with air pollution linked to thousands of premature deaths and immense NHS costs—the project focuses on how the narrative around air pollution has shifted from environmental concern to a pressing public health issue. Utilizing the influential setting of Great Ormond Street Hospital (GOSH), the research will investigate the effectiveness of education for the public and healthcare staff, feasible lifestyle interventions for vulnerable patients being treated at GOSH and integrate real-time air quality data into patient care.

Key research questions include: (1) how London’s public framing of air pollution has evolved; (2) the catalytic role of GOSH in advancing the public health agenda; and (3) the effectiveness of hospital-sponsored interventions on patient and community health. Methodologically, the project adopts a qualitative, challenge-led, and knowledge exchange approach. This includes semi-structured interviews with hospital staff, focus groups with patients and local communities, and co-designing a pollution lifestyle management programme aimed at reducing exposure and informing public health measures.

In close collaboration with GOSH, the PhD student will benefit from resources, training, and a three-month placement within GOSH’s air quality team—gaining insight into stakeholder engagement, data collection, and public health education. Outputs will include a communication manual for staff, a feasibility study of lifestyle interventions, and the development of digital tools to support families. The project will drive impact by connecting public health professionals, local authorities, and advocacy groups, with the aim of influencing policy and improving health outcomes for vulnerable populations. By integrating community perspectives and employing knowledge exchange methodologies, this research will establish a replicable model for public health engagement and sustainable partnerships, enhancing social science impact across the region.

Department of Psychology

Dr Nura Sidarus 

Reducing inequalities in access to treatments for depressive symptoms

Depression affects 1 in 10 people in the UK, with higher rates in young people, and growing rates in recent years. Only 1 in 3 adults with a common mental health problem are getting treatment; even after attempting several treatments, about 1/3 of individuals remain unwell. Moreover, there are significant inequalities in symptom prevalence as well as treatment access (by e.g. socioeconomic status, ethnicity, geography). Given its high economic and societal burden, it is imperative to better understand how to reduce these inequalities and increase access to effective treatments. Yet, one key barrier to progress remains the gap between research and clinical practice and policy, hindering the translation of evidence-based treatments into clinical practice, and restricting access to existing treatments.

This research project could involve work across:

A. Gathering evidence on existing inequalities in treatment access for common mental health problems, namely by:
- Leveraging existing data sources (electronic health records, other health datasets) to investigate access to specific treatments.
- Collecting new data on treatment access (and barriers to it), focused on specific target groups (e.g. those who remain unwell after several treatments; those with specific symptom profiles).

B. Investigating barriers in translation of this research to clinical practice, and policy making, namely:
- Identifying barriers in clinical practice to moving from a diagnostic focus to a person-centred, symptom-focused approach in treatment allocation.
- Building an understanding of the UK’s complex ecosystem of clinical guidelines, to inform impact-driven research and uncover different opportunities for influencing policy and practice.

This work thus aims to help leverage research for social benefit, and ultimately help reduce inequalities and widen access to evidence-based treatment options and choice for people living with depression and mental health problems.

We are particularly interested in applicants with a background in researching inequalities in (mental) health, epidemiological, qualitative and/or co-production research methods.

Department of Politics, International Relations, and Philosophy

Dr Jonathan Seglow

Free Speech

Free speech is rarely out of the headlines. Besides a steady stream of controversial statements made by politicians, celebrities and others, recent months have witnessed a larger-scale debate about the fate of free speech in the UK, the US and beyond. A substantial academic literature in law and political theory had explored the more philosophical concerns underneath these controversies. However, this literature is quite US focused and is only just catching up with recent developments in speech.

We invite PhD proposals which contribute to this literature, and connect it with contemporary debates about the politics of free speech. These might include one or more of:

- Foundations: What are the philosophical foundations of speech? Are longstanding notions like J. S. Mill’s truth argument and the marketplace of ideas still relevant? Do we need to rethink speech’s foundations in light of twenty-first century online speech?
- Hate Speech: Do the harms of hate speech speak in favour of greater regulation? And how are those harms best conceptualised, as assaults on dignity, incitement to violence, or acts of subordination?
- Democracy and Speech: Free speech is the life-blood of democracy, but should there nonetheless be limits on speech in democratic life e.g. speech which denies some groups’ basic equality? And if speech is valuable for democracy, does that imply there should be greater regulation of political speech e.g. spending caps on political campaigns?
- Misinformation: Does the right to free speech include the right to disseminate misinformation? How can we best encourage norms of critical scrutiny and truth-telling, and is there a role for legal regulation?
- Social Media: Is it reasonable for social media to be controlled by a few giant corporations? How should we combat the pathologies of the ‘attention economy’ and some voices drowning out others?

Department of Politics, International Relations, and Philosophy

Dr G. Anthony Bruno

The question whether thought and existence are ontological distinct

This doctoral project will defend G.W.F. Hegel’s philosophical view that thinking and existence are ontologically identical against Martin Heidegger’s view, recently defended in work by Robert Pippin, that they are ontologically distinct. To this end, it will follow the lead of Stephen Houlgate by relying primarily on Hegel’s systematic text ‘The Science of Logic’, thereby correcting the over-reliance on his relatively introductory text ‘The Phenomenology of Spirit’ by influential French commentators on Hegel (e.g., Jean Hyppolite, Alexandre Kojeve). In particular, this project will argue that Hegel’s idealist position, as presented in the ‘Logic’, is philosophically defensible, whereas Heidegger’s existentialist position, and its appropriation by Jacques Derrida, is not defensible. The argument will be that denying the ontological identity of thinking and existence, as Heidegger does, imposes a gap between thinking and existence. Such a gap would insuperable, since its putative removal would have to be thinkable—and yet thinking would by hypothesis would be assumed to be distinct and hence separate from existence. This would doom philosophy to infinitely longing for the identity of thinking and existence. But it is precisely and exclusively the identity of thinking and existence that allows for cognition, i.e., for thought to cognitively grasp what there is. The historical project of metaphysics depends on the cognizability of what there is (e.g., the cosmos), and abandoning this metaphysical project would signal an unacceptable skepticism. Hegel’s idealist position allows us to avoid such skepticism and, accordingly, his argument for this position warrants a charitable reconstruction and defence against Heidegger’s existentialist position, especially as defended recently by Pippin.

Department of Politics, International Relations, and Philosophy

Dr Andreu Casas

PhD Studentship on Social Media and Politics (London Social Media Observatory)

The London Social Media Observatory welcomes PhD applications on any topic related to social media and politics. Applications focusing on the curation and moderation of political content on social media platforms, and/or those proposing to use quantitative/computational methods will be given preference. Applicants are also encouraged to think about the societal impact of their research, and for example, about how some of the partners of the Observatory (such as Ofcom and the Electoral Commission) and policymakers would benefit from the research. The PhD candidate will work on their own project, but will also have the opportunity to collaborate with other projects and researchers within the Observatory. The PhD candidate will also benefit from Observatory resources, such as data engineer time to support their data collections and analyses, its wide networks of top international scholars, participation in other international projects, and funding for conferences and training. We strongly encourage applications from candidates of diverse backgrounds and experiences. We are committed to equality, inclusivity, and creating a welcoming environment where everyone can thrive and contribute to impactful research. Full details available here: https://www.royalholloway.ac.uk/media/hquftpqy/lsmo-phd-call-29spe2025.pdf. To apply, send the described material via email directly to Dr. Andreu Casas (andreu.casas@rhul.ac.uk). 

Department of Law & Criminology 

Professor Jill Marshall

Justice and acquisitions: Law and ethics in Kew’s overseas plant collecting

Research area: global justice, critical legal theory (incl. race theory), and legal history, archival databased records of Kew.
Collaborative partner in place: named expert at Kew Gardens (active proposal drafting has taken place throughout summer). Kew will provide access to their extensive records, previous internal research, supervision from their employee expert in international collections with a legal background.
Broad Research Questions fit with the African Union 2025 theme of the year “Justice for Africans and People of African Descent Through Reparations”. We seek to provide a wider basis for informed analysis of past museum practices from our unique access to Kew’s world-class botanical collections. These form the ideal basis for a nuanced study of justice and acquisitions, because of the completeness of Kew’s own archive, and its leadership in developing modern equitable frameworks among overseas botanic gardens. At the core of this project are rich archival records of expeditions relating to former British India and East Africa, not previously analysed from this perspective, and fully-databased records of all overseas collecting agreements made since 2000.
1. What evidence exists on the planning and organisation of RBG Kew’s plant-collecting expeditions and plant exports, 1840–1950? What agreements were made with in-country authorities?
2. How have these frameworks influenced present-day agreements and how do these now operate?
3. What international laws were/are relevant in this context and how did/do they relate to Kew’s own arrangements? What impact did/do these have?
4. What insights can be drawn from legal theoretical perspectives and scholarship in the fields of human rights law, and racial and ecosystems injustice, when used as frameworks for analysing Kew case studies?
5. What has been discovered to inform debates concerning restitution/ reparations/ remedies in the field of biodiversity?