Projects available
Applications are invited for the AI for Drug Discovery Programme for the available projects listed below.
We are delighted in this cross-faculty and cross-disciplinary training programme with our industrial partners to train the next generation of drug discovery researchers— Professor Michael Barnes, Professor of Bioinformatics. The William Harvey Research Institute, Faculty of Medicine and Dentistry
Please see below details of the available projects for the 2025-26 intake. Please note the application deadline as listed per project and ensure your application is submitted in-time. Available projects are open to candidates who meet the UKRI terms and conditions, and are classed as Home for tuition fee purposes. Further details in the project descriptions below.
Each project has a supervisor based at Queen Mary, and engagement from Industry, including the option for a placement. The level of industry engagement varies depending on the nature of the project. We suggest you review each project description to learn more about the proposed research. Once you have identified your top project, you can submit an application via the Apply page. Note, you will be asked to identify your chosen project, and a maximum of 1 other project; you cannot apply for more than 2 projects, so we recommend you consider your choice carefully, ensuring that it is the right fit for you and your research aspirations.
Points to consider when reviewing projects:
- Is the project a good fit for my research experience to-date, and my research interests?
- Do I have the necessary background knowledge, or could I reasonably acquire this through targeted training on the programme?
- What attracts me to this project, and which part of the project most excites me?
- Does the supervisory team seem a good fit for me, and what makes me want to work with them?
AI-enabled Vulnerability Assessment of Fungal Proteins using Multi-omics
AI-enabled Vulnerability Assessment of Fungal Proteins using Multi-omics and Multi-relational Knowledge Graphs
Application deadline: August 31st 2025
Background
We are looking for an enthusiastic data scientist, with a passion for biological research, to pursue a 4-year fully funded PhD studentship aimed at assessing the vulnerability of fungal proteins. Specifically, you will develop, apply and evaluate foundation models and graph neural network (GNN) methods to predict the half-life of proteins in a range of commercially important non-model organisms. This PhD offers a unique opportunity to develop a research career in applied AI, among likeminded people with expertise in knowledge graphs, deep learning and bioinformatics.
This entirely computer-based project will be based in Prof Conrad Bessant’s lab within the Digital Environment Research Institute at Queen Mary University of London, with extensive support from Syngenta UK. The project will build on the Bessant lab’s prior work on multi-omic multi-relational graph neural networks, and leverage Syngenta’s world leading expertise in the molecular biology of fungal pathogens. Data for the project is already available in the public domain, and in proprietary Syngenta datasets.
This project will be a collaborative effort between Queen Mary University of London, and Syngenta. Students will benefit from exposure to both academic and industry research environments, gaining a comprehensive perspective on the challenges and opportunities in computational biology from both fundamental and translational viewpoints.
Syngenta Crop Protection is a leader in agricultural innovation, bringing breakthrough technologies and solutions that enable farmers to grow productively and sustainably. We offer a leading portfolio of crop protection solutions for plant and soil health, as well as digital solutions that transform the decision-making capabilities of farmers.
Eligibility and Applying
This project is part of the UKRI/BBSRC AI for Drug Discovery Progamme, and successful candidates will join a cohort of students working on complementary projects in the AI for Drug Discovery space.
We are looking for highly motivated individuals who are passionate about contributing to new discoveries in drug discovery bioscience through the application of the latest techniques in AI and data science. Ideal candidates will have a grounding in both a natural science and data science, e.g. through a Master's degree or work experience in a subject such as bioinformatics or computational chemistry. Alternatively, you may have, for example, a first-class degree in computer science followed by biochemistry experience, or vice versa (qualifications and evidence thereof must be obtained before October 2025). You will be confident in performing data wrangling and analysis in a language such as Python, R or C++. Effective communication skills are essential.
We particularly encourage students from groups that are currently underrepresented in postgraduate science research, including black and minority ethnic students and those from a socio-economically disadvantaged background.
The Studentship will cover tuition fees, UKRI stipend (expected to be £22,780) and a consumable allowance for a period of 4-years (pro-rata for part-time).
Please see the Apply Pages for further details on how to submit an application. Applications deadline: August 31st 2025
SUPERVISORS:
Prof Conrad Bessant - Professor of Bioinformatics, QMUL
Dr Oscar Charles - Computational Biologist, Syngenta
Dr Helena Saunders – Head of Bioinformatics Research, Syngenta
Project Partner: Syngenta