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Digital Environment Research Institute (DERI)
  1. Digital Environment Research Institute (DERI)
  2. UKRI AIDD Doctoral Training Programme
  3. Projects

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?

Domain Adaptation to Map Gene Expression Across Species, Life Stage and cell-type in Economically Important Crop Pests

  • Start date: By 30th September 2026
  • Application deadline: 30th July 2026
  • Open to applicants with home fee status only.

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 developing deep learning models for transcriptomic data in crop pests. Specifically, you will develop, apply and evaluate foundation models and domain adaptation techniques to predict the expression profiles of targeted pests across life stages and cell types. This PhD offers a unique opportunity to develop a research career in applied AI, among like-minded people with expertise in genomics, deep learning and bioinformatics.

This entirely computer-based project will be based in Dr Matteo Fumagalli’s lab within the Centre for Evolutionary and Functional Genomics at Queen Mary University of London (QMUL), with extensive support from the Digital Environment Research Institute at QMUL and Syngenta UK. The project will build on the Fumagalli lab’s prior work on machine learning in genomics, 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.

Eligibility

We are seeking a highly motivated individual with a proven track record of success in both data science and biology, preferably in genomics. The ideal candidate will have a solid understanding of molecular biology, a high-grade MSc/MRes degree in a subject such as bioinformatics, data science or applied AI, and some data-driven research experience (e.g. a relevant MSc research project).

Applicants must be available to start their PhD by 30th September 2026.

Due to funding terms and availability, this project is open to applicants eligible for home fee status only.

Supervisors

  • Dr Matteo Fumagalli – Senior Lecturer & Head of Department, QMUL
  • Dr Oscar Charles – Computational Biologist, Syngenta
  • Dr Chris O’Grady – Computational Biologist, Syngenta

Informal Queries

For more details of the project, or to discuss your eligibility, feel free to contact Dr Matteo Fumagalli (m.fumagalli@qmul.ac.uk).

About QMUL/DERI

At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the previously unthinkable. Throughout our history, we’ve fostered social justice and improved lives through academic excellence. And we continue to live and breathe this spirit today, not because it’s simply ‘the right thing to do’ but for what it helps us achieve and the intellectual brilliance it delivers. We continue to embrace diversity of thought and opinion in everything we do, in the belief that when views collide, disciplines interact, and perspectives intersect, truly original thought takes form.

The Digital Environment Research Institute (DERI) is Queen Mary’s flagship University Research Institute dedicated to ground-breaking multi-disciplinary research in digital and data science, including artificial intelligence (AI). DERI offers an outstanding research environment including a dedicated physical space along with recently purchased high performance computing infrastructure to enable scientific breakthroughs. Further, DERI leads the University’s participation in The Alan Turing Institute, the UK national institute for data science and AI.

About Syngenta

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.

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