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AI/ML Postdoctoral Fellow - F Rouhani Lab

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The Francis Crick Institute

1 Midland Rd, London NW1 1AT, UK

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AI/ML Postdoctoral Fellow - F Rouhani Lab

Short summary

We are seeking an ambitious Postdoctoral Fellow to lead a cutting-edge computational project investigating how driver mutation clones interact with their microenvironment in chronic liver disease and liver cancer.

This is a highly collaborative and cross-institutional role between the Francis Crick Institute and the Wellcome Sanger Institute. Working closely with the Lotfollahi Lab – leaders in generative AI and foundation models for spatial and single-cell genomics – you will develop and apply state-of-the-art machine learning approaches to large-scale spatial genomics and multi-modal biological datasets.

Key Responsibilities

These include but are not limited to:

  • Developing advanced AI/ML methods for analysing spatial genomics and histology datasets.
  • Applying graph neural networks, transformer models and generative AI approaches to study clone-microenvironment interactions.
  • Integrating spatial transcriptomics, single-cell sequencing and imaging datasets.
  • Designing benchmarking strategies and reproducible computational workflows.
  • Performing clonal reconstruction and spatial mapping analyses from genomic datasets.
  • Collaborating closely with computational scientists, clinicians and experimental researchers across the Crick and Sanger Institute.
  • Leading publications, conference presentations and dissemination of research findings.

About you

You will have:

  • PhD (or near submission) in computational biology, machine learning, computer science, statistics or a related quantitative discipline.
  • Experience developing and applying deep learning or AI/ML methods to complex scientific datasets.
  • Strong programming and scientific computing skills in Python (e.g. numpy, pandas, PyTorch and/or JAX).
  • Experience analysing complex biological, imaging or spatial datasets, or strong evidence of rapidly adapting to new data domains.
  • Excellent communication, organisational and collaborative working skills.
  • Ability to work effectively within interdisciplinary and cross-institutional research teams.
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