Postdoctoral Fellow - Bioinformatics & Executable Modelling for Rare Disease Digital Twins

Postdoctoral Fellow - Bioinformatics & Executable Modelling for Rare Disease Digital Twins

EMBL-EBI - European Bioinformatics Institute

Hinxton, United Kingdom

We are looking for a motivated and talented postdoctoral-level researcher with experience in executable modelling to join a cutting-edge project developing Digital Twins for rare diseases. This is a unique opportunity to work at the intersection of systems biology, AI, and translational research, and to contribute to open science through the Chan Zuckerberg Initiative.

The project

Digital Twins are virtual representations of patients that simulate disease progression and treatment response. Rare diseases pose a unique challenge due to limited patient data - especially at the single-cell level - making traditional modelling approaches difficult. This project tackles that challenge by integrating multi-omics and clinical data using hybrid models combining mechanistic, GenAI, and machine learning approaches.

You’ll contribute to building disease-specific Digital Twins using large-scale single-cell multi-omics datasets, mechanistic simulations, and predictive AI models. Your work will help unlock new insights into disease mechanisms and inform potential treatments, diagnostics, and drug repurposing opportunities.

Your role

You will develop and apply methods to transform omics data into networks and executable models, collaborating closely with experts across the Petsalaki and Sheriff groups, Open Targets, EMBL-EBI, and the wider rare disease and biocuration community. You will be primarily supervised by the Petsalaki group (Whole cell sigalling) and the Sheriff team (Biomodels). The Petsalaki group develops data driven network inference and modelling approaches from large omics datasets and the Sheriff team leads the development of innovative modelling approaches and maintenance of the Biomodels database.

Key responsibilities include:

  • Generation of phenotype-specific networks from bulk-RNAseq and scRNAseq data from rare disease patients;
  • Building executable models (Boolean, ODE, agent-based or others) from omics data;
  • Collaborating closely with data curators, multi-omics data scientists and AI engineers to integrate and enrich disease datasets, and test and validate models;
  • Applying hybrid modelling approaches to limited data scenarios;
  • Enabling multi-scale and cross-disease modelling for hypothesis generation and therapy discovery;
  • Ensure FAIR principles in your outputs and contribute to the open source community by sharing models (where possible) in BioModels and other repositories;
  • Generation of synthetic data to represent rare disease patients that can be shared.

You have

  • A PhD in bioinformatics, physics, or a related data-intensive field;
  • Proficiency in Python (or R), version control, and clean code practices;
  • Experience with omics data analysis and integration;
  • Hands-on expertise in developing and fitting executable models;
  • Strong communication and teamwork skills.

You may also have

  • Network inference and analysis experience;
  • Machine learning experience;
  • Track record of completed research projects (e.g., publications, tools).

Apply NowDeadline 13 July

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