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.
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.
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.
Apply NowDeadline 13 July