Postdoc: Mechanistic Modelling for Rare-Disease Twins

Earlham Institute; Norwich; Presencial; GBP 39.000 - 47.000; Jornada completa

Descripción de la vacante

The Earlham Institute in Norwich, UK, invites applications for a full-time Postdoctoral Research Scientist (SC6) to lead mechanistic systems biology modelling within a Chan Zuckerberg Initiative collaboration on open-source Digital Twins for rare diseases.

You will develop executable models, adapt literature-based networks and use AI/ML to create hybrid methods, with opportunities to expand into other modelling paradigms and multi-omics integration.

Formación

• PhD in a quantitative discipline.
• Strong experience in mechanistic modelling of biological systems.
• Experience with dynamical models is essential.
• Experience with omics, machine learning, BioModels/community standards or additional modelling paradigms would be advantageous.
• Strong scientific programming skills.

Responsabilidades

• Lead the mechanistic systems biology modelling component of the project, developing executable models and reusable Digital Twin models.
• Collaborate with AI/ML researchers to develop hybrid mechanistic-AI methods and personalise models using multi-omics data.
• Perform in-silico perturbations, quantify uncertainty and validate model predictions.
• Contribute to reusable open-source Digital Twin software and reproducible model dissemination.

Conocimientos

Mechanistic modelling

Dynamical modelling

Scientific programming

AI/ML integration

Omics experience

Machine learning

BioModels/community standards

Educación

PhD in a relevant quantitative discipline

Descripción del empleo

The Earlham Institute in Norwich, UK, invites applications for a full-time Postdoctoral Research Scientist (SC6) to lead mechanistic systems biology modelling within a Chan Zuckerberg Initiative collaboration on open-source Digital Twins for rare diseases.

You will develop executable models, adapt literature-based networks and use AI/ML to create hybrid methods, with opportunities to expand into other modelling paradigms and multi-omics integration.