Master Thesis - Efficient Markov Chain Monte Carlo Techniques for Studying Large-scale Metaboli[...]

TN Germany; Jülich; Vor Ort; EUR 30.000 - 50.000; Vollzeit

Zusammenfassung

An innovative opportunity awaits you in the realm of Systems Biotechnology. Join a forward-thinking team focused on advancing Markov Chain Monte Carlo techniques for large-scale metabolic modeling. This role offers a unique chance to work on impactful projects, gain hands-on experience, and collaborate with a diverse, international group of experts. You'll be at the forefront of developing algorithms and implementing them on cutting-edge supercomputers. If you are passionate about mathematics, data science, and making a difference, this position is perfect for you!

Leistungen

Flexible working hours

Access to excellent technical equipment

Qualified supervision and support

Collaborative international team

Qualifikationen

• Strong practical skills in C++ and Python programming.
• Interest in probability theory, mathematics, and data science.

Aufgaben

• Adapt MCMC methods to metabolic flux inference.
• Develop tailored algorithms and validate through case studies.

Kenntnisse

C++ Programming

Python Programming

Probability Theory

Data Science

Ausbildung

Master's Degree in relevant field

Jobbeschreibung

Job Title: Master Thesis - Efficient Markov Chain Monte Carlo Techniques for Studying Large-scale Metabolic Models, Jülich

Job Details:

• Client:
• Location:
• Job Category: Other
• EU work permit required: Yes
• Job Reference: 22a4410e29b0
• Job Views: 1
• Posted: 28.04.2025
• Expiry Date: 12.06.2025

Job Description:

Your Job:

Quantifying the activity of enzymes within large-scale biochemical networks is a fundamental challenge in Systems Biotechnology. This involves inferring unknown parameters from models that are incomplete and data with errors. Bayesian analysis using Markov Chain Monte Carlo (MCMC) has become the gold standard for such challenges.

For high-dimensional parameter inference with Bayesian statistics, powerful MCMC methods like differential evolution and the Riemann Manifold Langevin Monte Carlo have been proposed. However, due to the specific structure of inference problems in metabolic models, direct application of these algorithms is not feasible.

In this project, you will adapt MCMC methods to metabolic flux inference, develop tailored algorithms inspired by existing ones, implement them in an existing C++ framework, and validate them through realistic case studies. The focus can be on advancing the mathematical theory of MCMC, implementing code for Jülich supercomputers (GPU/CPU), or integrating with practical modeling projects.

Your Profile:

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

Our Offer:

• Work on impactful, future-oriented topics with societal relevance.
• Gain practical experience alongside your Master studies.
• Collaborate within an international, committed, and collegial team.
• Access to excellent technical equipment and the latest technology.
• Qualified supervision and support.
• Independently prepare and work on your tasks.
• Flexible working hours and location.

We value diversity and inclusion. Applications from individuals of all backgrounds, ages, genders, disabilities, sexual orientations, social, ethnic, and religious origins are welcome.

Application Note: The position will be advertised until filled. We encourage early applications.

Questions? Contact us via our contact form. Please note applications via email are not accepted for technical reasons.