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.

Your group
Petsalaki Group, in collaboration with Open Targets and BioModels teams.

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 experienc

- Machine learning experience

- Track record of completed research projects (e.g., publications, tools)

Benefits and Contract Information

- Financial incentives: depending on circumstances, monthly family/marriage allowance of £272, monthly child allowance of £328 per child. Generous stipend reviewed yearly, pension scheme, death benefit, long-term care, accident-at-work and unemployment insurances
- Hybrid working arrangements
- Private medical insurance for you and your immediate family (including all prescriptions and generous dental & optical cover)
- Generous time off: 30 days annual leave per year, in addition to eight bank holidays
- Relocation package
- Campus life: Free shuttle bus to and from work, on-site library, subsidised on-site gym and cafeteria, casual dress code, extensive sports and social club activities (on campus and remotely)
- Family benefits: On-site nursery, child sick leave, generous parental leave, holiday clubs on campus and monthly family and child allowances
- Contract duration: This position is a 2 year fixed term grant limited contract
- Salary: Year 1 Stipend at rate of £3,307 (Total package will be dependant on family circumstances)
- International applicants: We recruit internationally and successful candidates are offered visa exemptions. Read more on our page for international applicants.
- Diversity and inclusion: At EMBL-EBI, we strongly believe that inclusive and diverse teams benefit from higher levels of innovation and creative thought. We encourage applications from women, LGBTQ+ and individuals from all nationalities.
- Job location: This role is based in Hinxton, near Cambridge, UK. You will be required to relocate if you are based overseas and you will receive a generous relocation package to support you.
- DORA - EMBL is a signatory of DORA and is committed to hiring and training outstanding research, service, and administrative personnel.