Developing Advanced Bayesian Methods for Metabolic Flux Inference

vor 3 Wochen


Jülich, Nordrhein-Westfalen, Deutschland Forschungszentrum Jülich Vollzeit

Project Overview:

Quantifying the activity of enzymes in large-scale biochemical networks is a fundamental challenge in Systems Bio(tech)nology. Bayesian analysis using Markov Chain Monte Carlo (MCMC) has become the gold standard for addressing high-dimensional parameter inference problems. However, direct application of existing MCMC algorithms to metabolic models is not possible due to their specific structure.

Your Role:

You will bring MCMC methods into the setting of metabolic flux inference and develop tailored algorithms inspired by existing methods. You will implement these algorithms in an existing C++ framework, validate, and benchmark them with a realistic case study.

Requirements:

  • Highly motivated with an interest in probability theory, mathematics, and data science.
  • Excellent practical C++ and Python programming skills.
  • Strong interest in curiosity-driven multidisciplinary research.

What We Offer:

We work on the latest issues impacting society and offer you the chance to actively contribute to shaping the change. We support you in your work with:

  • An interesting and socially relevant topic for your thesis with future-oriented themes.
  • Ideal conditions for gaining practical experience alongside your Master studies.
  • An interdisciplinary collaboration on projects in an international, committed, and collegial team.
  • Excellent technical equipment and the newest technology.
  • Qualified supervision and support.
  • The chance to independently prepare and work on your tasks.
  • Flexible working hours as well as working location.

We welcome applications from people with diverse backgrounds and offer a diverse and inclusive working environment with equal opportunities.



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