Postdoctoral Fellow - Frohlich Lab

Salary for this Role:
From £47,500, with benefits, subject to skills and experience.

Job Title:

Postdoctoral Fellow - Frohlich Lab.

Closing Date: 24/Sept/ GMT

Contract term:
This is a full-time, fixed term (4 years) position on Crick terms and conditions of employment.

Reporting to:
Fabian Fröhlich, Group Leader.

The Francis Crick Institute is Europe’s largest biomedical research institute under one roof. Our world-class scientists and staff collaborate on vital research to help prevent, diagnose and treat illnesses such as cancer, heart disease, infectious diseases and neurodegenerative conditions. The Crick is a place for collaboration, innovation and exploration across many disciplines. A space where the brightest minds can pursue big and bold ideas and discover answers to crucial scientific questions.

We support them in a dynamic environment which fosters excellence with state-of-the-art infrastructure, cutting-edge facilities, and a creative and curious culture. We’ve removed traditional boundaries of departments, divisions and disciplines and instead have an open approach that supports every researcher. This gives us the freedom to take risks and carry out high-quality, pioneering research. Creating a space for discovery without boundaries helps us to turn our science into benefits for human health and the economy.

The Fröhlich Lab combines quantitative biology with scientific machine learning (SciML) to understand how cells respond to perturbations and uncover fundamental principles of cellular behaviour. Bringing together expertise across mathematics, physics, biology, engineering and data science, we integrate mechanistic models, machine learning and high-throughput experiments to study cellular signalling and cell states across scales.

We are seeking an ambitious Postdoctoral Fellow to develop the next generation of deep mechanistic models (DMMs; Fabrini & Fröhlich, bioRxiv 2026, doi: 10.64898/.741759) for biological systems. You will lead a computational research project extending DMMSs from population-level to single-cell data, combining neural networks with mechanistic differential equation models to understand signalling differences between individual cells. You will develop open-source computational tools () and apply them to cutting-edge single-cell datasets generated within the lab and from public resources.

There is significant scope to shape the project around your interests, including live-cell imaging, flow matching, optimal transport or cell-cycle modelling. This is an exciting opportunity for a computational researcher who wants to develop innovative machine-learning methods while tackling fundamental biological questions within a highly interdisciplinary environment.

• Leading an independent research project in scientific machine learning and mechanistic modelling.
• Developing deep mechanistic models for single-cell biological data.
• Building and maintaining reusable, open-source computational tools.
• Applying new methods to biological datasets and interpreting their biological significance.
• Collaborating with computational and experimental researchers across the Crick and internationally.
• Publishing your research and presenting at national and international conferences.
• Supporting and mentoring graduate students where appropriate.

• PhD in mathematics, physics, data science, engineering, systems biology or a related field, or be in the final stages of PhD submission.
• Strong scientific programming skills in Python.
• Knowledge of mathematical models of biological systems and numerical methods for differential equations.
• Experience developing and pursuing independent research ideas.
• Strong written and verbal scientific communication skills.
• A collaborative, cross-disciplinary approach and interest in applying computational methods to biological questions.
• Commitment to reproducible research and open science.

• Experience with machine learning frameworks such as JAX or PyTorch with a focus on single-cell methods such as optimal transport or flow matching.
• Knowledge of cell biology, biochemistry relevant to signalling…