Postdoctoral Fellow - Bioinformatics & Executable Modelling for Rare Disease Digital Twins EMBL-EBI - European Bioinformatics Institute Hinxton, United Kingdom

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.,

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