Novel AI methods for identifying cellular communities in spatial transcriptomics data of cancer tissues

In this PhD project you will join an international team of scientists to develop novel computational approaches for cancer diagnosis, prognosis, and treatment response.

Start date

1 October 2023


4 years

Application deadline

Funding source

Faculty of Health and Medical Sciences at the University of Surrey and Agency for Science, Technology and Research (A*STAR) in Singapore

Funding information

Whilst the candidate is in the UK, the University of Surrey funding will include UKRI-aligned stipend (£17,668 pa for 2022-23) and approved fees. Whilst in Singapore, funding from A*STAR will consist of a stipend (inclusive of housing subsidy) of SGD 3300/month, a one-time airfare grant to Singapore of SGD 1,500, a one-time settling-in allowance in Singapore of SGD 1,000, a one-time IT allowance of SGD 800, medical insurance and a conference allowance.


In this PhD project you will join an international team of scientists to develop novel computational approaches for cancer diagnosis, prognosis, and treatment response.

The aim of the project is to integrate image and spatial transcriptomic data to gain insight into cancer tumour cellular composition, spatial organisation, and cell-cell communication patterns.

Together with our team of collaborators, you will develop novel computational methods to identify communities of cells in the (3D) physical space of tumour tissue. You will harness the power of spatial transcriptomics to map transcriptional activity and reveal complex tissue architecture with nearly single-cell resolution.

Our challenge is to develop novel computational methods that can identify spatially resolved clusters of cell types and cell-states from hierarchical high dimensional transcriptomic data. We will explore and enhance unsupervised spatial clustering methods, design novel cost functions, contrast unsupervised with supervised methods, and compare results in different analytical platforms.

New technologies for spatial biology are being actively developed and are becoming more widely available. Spatial transcriptomics data is hierarchical, high dimensional, spatially organised, and massive. Due to the volume and complexity of these data, new computational approaches are required for translating data into actionable insights. Our project aims at addressing these challenges by developing and applying machine learning and statistical techniques to accelerate the process of extracting key cell organisation and cell-cell interaction features from image and transcriptomic data to aid in the diagnosis, prognosis, and response to treatment.

The candidate should have strong computational background and an interest in spatial biology.

For further information please email Dr Alex Couto Alves ( and Dr Adaikalavan Ramasamy (

Eligibility criteria

Open to UK and international students with the project starting in October 2023.

You will need to meet the minimum entry requirements for our Biosciences and Medicine PhD programme.

How to apply

Applicants are strongly encouraged to contact the relevant principal supervisor(s) to discuss the project(s) before submitting their application.

Applications should be submitted via the Biosciences and Medicine PhD programme page (N.B. Please select the October 2023 start date when applying).

You may opt to apply for 1, 2 or all 3 of the studentship currently available projects via this joint programme.

When completing your application, in place of a research proposal, please provide a brief motivational document (1-page maximum) that specifies:

  • The reference numbers(s) for the project or projects you are applying for,
  • The project title(s) and principal supervisor name(s),
  • If applying for more than one of the projects, please also indicate your order of preference,
  • An explanation of your motivations for wanting to study for a PhD, and
  • An explanation of your reasons for selecting the project(s) you have chosen.

Additionally, to complete a full application, you MUST also email a copy of your CV and 1-page motivational document directly to the relevant project principal supervisor of each project you apply for. Due to short turnaround times for applicant shortlisting, failure to do this may mean that your application is not considered.

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