Project Call

We welcome discussions with companies interested in developing an industry-supported CASE studentship through the BBSRC Doctoral Focal Award in Advanced AI for Multi-modal Spatial Biology. Industry partners can co-design the PhD project with QMUL researchers, contribute to the student’s supervision, and host the student for a placement. This provides an opportunity to develop research addressing areas of shared scientific and industrial interest.

We take a flexible approach, with the company’s financial contribution and intellectual property arrangements agreed according to the nature of the project and the partner’s requirements. Industry partners are normally expected to meet reasonable travel and accommodation costs associated with the placement and provide any additional financial support agreed for the placement. A contribution towards project consumables may also be required.

Companies interested in discussing a potential studentship should contact spatialai-dfa@qmul.ac.uk.

This DFA exists to develop outstanding, independent future leaders in computational approaches for spatial biology, developing the latest data science, AI and machine learning techniques. The students will create new models and tools to understand how molecules and cells interact to control tissue function, using data comprised of both visual images and single-molecule measurements.

We are guided by the following values:

• Impact – research that has relevance to the DFA's mission.
• Excellence – scientifically robust, ambitious, yet achievable PhD projects.
• Inclusion – a positive, safe, and collegiate research culture.
• Development – recognising the PhD as a training vehicle for research professionals.

All submitted projects must be fully and accurately costed at the application stage by the supervisory team and not via the Joint Research Management Office.

Any awarded Research Training Support Grant (RTSG) is intended to cover the eligible costs of the proposed project.

In cases where project costs exceed the maximum RTSG award, it is the responsibility of the supervisor(s) to identify, secure, and apply additional funding from independent sources. This information must be included in the project proposal.

By submitting a project to the DFA, supervisors commit to:

• Providing high‑quality academic supervision and research leadership.
• Supporting the student’s training, cohort engagement, and career development.
• Fostering open communication, mutual respect, and ethical research practice.
• Prioritising the student’s development and wellbeing alongside research goals.
• Actively contributing to the wider DFA community where required.

• Each Supervisory team must include demonstrable expertise in both: advanced AI computational-method development; and spatial biology, bioinformatics, bioengineering or a closely relevant biomedical discipline
  
• The project team must ensure that access to high-quality, sufficiently scaled data is secured and available before the commencement of the PhD project.
• Both primary and secondary supervisors must be research‑active, primarily appointed by Queen Mary University of London in accordance with Section 44 and 46 of the QMUL Code of Practice for Research Degree Programmes.
• If a project is selected by an incoming student, the primary supervisor must confirm that, from the student’s date of entry, they will not exceed the maximum supervisory load of eight students as primary supervisor, in line with Section 47 of the QMUL Code of Practice for Research Degree Programmes.
• The supervisory team are required to have read, reviewed, and commit to upholding Section 55 (“Responsibilities of Supervisors”) of the QMUL Code of Practice for Research Degree Programmes.
• The supervisory team must have completed, or be registered to complete, the required PGR Supervisor Training and are responsible for ensuring their training remains current: https://www.qmul.ac.uk/doctoralcollege/supervisors/training/

PhD project proposals will be evaluated in two parts against the following criteria.

Alignment with DFA's Vision and Mission

• Alignment: Does the PhD project align with the UKRI BBSRC's remit, programmes, and priorities.
• Access: Requisite spatial biology data for the project is available at the outset.
• Future: Does the project demonstrate a clear pathway for the student’s future career development.

Scientific Quality and Training Potential of the PhD Project

• Clear, important research question: Addresses a meaningful gap in current knowledge.
• Strong conceptual framework: Hypothesis-driven, mechanistic, not purely descriptive.
• Originality and novelty: Advances the field rather than incrementally repeating known work.
• Feasibility with impact balance: Ambitious but realistically achievable within 3.5 years. (The core research programme should be achievable within approximately 3.5 years, leaving sufficient time for cohort training, professional development and thesis completion within the four-year studentship.)
• Technical skill development: Opportunities to master relevant experimental and/or computational methods.
• Transferable skills acquisition: Training in data analysis, scientific writing, presentation, and critical thinking.
• Expert tuition and guidance: in AI software development, data quality and biological interpretation, together with comprehensive instruction in state-of-the-art Artificial Intelligence and Spatial Biology techniques.

Proposals Within Remit:

• Proposals that advance machine-learning methods and develop software to deliver new analysis options.
• Clear feasibility, defined by mandatory data availability by start of PhD.
• Proposal can include meta-analysis across addtional datasets.
• Suitable data can be multi-plex image-based spatial based, and/or multi-modal data integration that includes a molecular imaging component.
• Support the cohort effect through a common focus on image-based technologies.
• The project must make a substantive and generalisable methodological contribution in AI, rather than using established computational tools solely to answer a single biological question.

Proposals Not Within Remit:

• Proposals that require the collection of new spatial data or need bench experiments to be performed, i.e. the data must already exist, or be due to be produced in its entirety by September 2027 (for cohort 2).
• Proposals that have not clearly defined the project datasets or rationale for developing state-of-the-art AI approaches.
• Projects primarily concerned with applying established analytical tools without substantive computational-method development.

As part of the project call submission, a Diversity Monitoring Form must be completed alongside the Project Proposal Form. You may select “prefer not to say” for any question.

• Can I submit more than one project proposal?

Answer: Supervisors may submit only one project proposal as the primary supervisor. However, a supervisor may be listed as a secondary supervisor or as part of the supervisory team on multiple projects.

• Can I submit a project proposal if I currently supervise a DFA DTP student?

Answer: Yes. If you are currently the primary supervisor of a DFA DTP student, you may submit a new project proposal as a secondary supervisor. If you are currently a secondary supervisor, you may submit a new project proposal as the primary supervisor.

• I am not primarily employed by QMUL. Can I still submit a project proposal?

Answer: No. Funding can only be awarded to Principal Investigators who are employed by QMUL, however you may be eligible to become part of a supervisory team.

• I already have a project listed in the 2026 project catalogue. Will it automatically carry over to the next catalogue if it isn’t selected by a 2026 entry student?

Answer: No. Projects will not automatically be carried forward to the next catalogue. Supervisors will be asked to review and update their project proposals to ensure they reflect the latest developments in the research area, current data and resource availability, and any recommendations provided by the project review panel.