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Computational Biology and Machine Learning● OpenPostdocPreview

AI/ML Postdoctoral Fellow

Lead computational research investigating clone-microenvironment interactions in liver disease using advanced machine learning.

Best for:Best for quantitative researchers with strong Python and deep learning skills interested in applying AI to spatial genomics and cancer biology.

Francis Crick Institute · London, United Kingdom · R2 · Recognised researcher
More professors at Francis Crick Institute
Spatial GenomicsMachine LearningSingle-Cell TechnologiesCancer BiologyGenerative AI
This is a postdoc position — not a PhD studentship.
Deadline
Nov 24, 2026
39 days
Funding
Salaried
£47.5k/yr
Start date
flexible
Fixed-term
Location
London
United Kingdom

Overview

The Francis Crick Institute invites applications for a four-year Postdoctoral Fellow to join the Tissue Regeneration and Clonal Evolution (TRCE) Laboratory. Working in close collaboration with the Wellcome Sanger Institute and the Lotfollahi Lab, the successful candidate will develop and apply cutting-edge machine learning methods to large-scale spatial genomics and multi-modal biological datasets. The role focuses on investigating how driver mutation clones interact with their microenvironment in chronic liver disease and cancer. The researcher will be embedded across both institutes and is expected to drive high-impact computational research, collaborate with interdisciplinary teams, and lead manuscript preparation.

Postdoctoral FellowArtificial IntelligenceMachine LearningSpatial TranscriptomicsGraph Neural NetworksComputational BiologyGenomics

What you'll do

  • Develop advanced AI and machine learning methods for analyzing spatial genomics and histology datasets.
  • Apply graph neural networks, transformer models, and generative AI approaches to investigate clone-microenvironment interactions.
  • Integrate spatial transcriptomics, single-cell sequencing, and imaging datasets into unified workflows.
  • Design robust benchmarking strategies and reproducible computational pipelines.
  • Perform clonal reconstruction and spatial mapping analyses from complex genomic datasets.
  • Collaborate closely with computational scientists, clinicians, and experimental researchers across multiple research institutes.
  • Lead publications, conference presentations, and the broad dissemination of research findings.

Requirements

Degree required: PhD (or near submission) in computational biology, machine learning, computer science, statistics, or a related quantitative discipline

Language: English

Instruction: English

Required skills
  • Experience developing and applying deep learning or AI/ML methods to complex scientific datasets
  • Strong programming and scientific computing skills in Python including numpy, pandas, PyTorch and/or JAX
  • Experience analyzing complex biological, imaging, or spatial datasets, or proven ability to adapt rapidly to new data domains
  • Excellent communication, organizational, and collaborative working skills
  • Ability to work effectively within interdisciplinary and cross-institutional research teams
Nice to have
Experience with spatial transcriptomics or single-cell genomics analysisFamiliarity with graph neural networks, transformers, generative AI, or foundation modelsExperience working with cloud or HPC environments, workflow orchestration, or reproducible computational pipelinesPublications or presentations at leading computational biology or machine learning conferencesDomain interest in cancer biology, tissue regeneration, or translational genomics
Eligibility
  • Applicants must hold a PhD or be near submission in a relevant quantitative or computational discipline
  • Eligible for visa sponsorship to work in the United Kingdom

Funding & terms

Type
Salaried
Amount
£47.5k/yr
Duration
4 years
Programme
Francis Crick Institute
Francis Crick Institute

How to apply

Deadline: 2026-11-24

Candidates must submit their application through the online portal ensuring they meet the essential criteria outlined in the role profile. For queries regarding the application process or requests for reasonable adjustments, applicants can contact the designated HR representative.

Documents required
  • CV
  • Role profile application
↗Apply on site

Contact

Ruby Oluwafemi· HR Contact

ruby.oluwafemi@crick.ac.uk

FR
Prof.
Foad Rouhani
Tissue Regeneration and Clonal Evolution Laboratory
Lab
TRCE Lab
Francis Crick Institute
United Kingdom
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