PhD position - Probabilistic Metabolic Flux Analysis within the HDS-LEE graduate school

Your Job :

This PhD project develops a Bayesian inference framework for hybrid model- and data-driven modeling of metabolism, with a particular focus on handling model misspecification. By combining Bayesian computational statistics, differentiable programming, and high-performance computing, the project aims to deliver robust, interpretable, and scalable methods for metabolic flux analysis.

You will :

Design hierarchical models that explicitly capture misspecifications in metabolic models

Develop differentiable and scalable inference algorithms using automatic differentiation

Implement HPC-tailored sampling strategies in Python and C++

Apply your framework to analyse real biological datasets to demonstrate robustness, interpretability, and practical impact

Contribute to open-source software tools, helping to shape future research infrastructure

• Present your results on conferences in Germany and abroad

Your Profile :

Excellent Master’s degree in statistics, physics, mathematics, or a related quantitative field, ideally with a strong focus on computational practice

Strong mathematical and statistical background, with pronounced analytical and problem-solving skills

Proven programming expertise in Python and C++, with solid experience in scientific computing and software development; familiarity with Linux environments

Excellent collaboration and communication skills and enjoyment of working in an international, interdisciplinary research team

Familiarity with Bayesian thinking is desirable

No prior biological experience is required; curiosity for life science questions and willingness to collaborate with experimentalists is sufficient