# Master Thesis - Efficient Markov Chain Monte Carlo Techniques for Studying Large-scale Metabolic Models As a leading research institution for microbial biotechnology the Institute of Bio- and Geosciences – Biotechnology (IBG-1) focuses on the development of biotechnological processes for the sustainable bio-based production of pharmaceutical and chemical products. We investigate how microorganisms and isolated enzymes can be used to produce a variety of products from renewable raw materials. IBG-1 is a leading institution in process development for industrial biotechnology with increasingly miniaturized and automated experiments. The institute provides an excellent infrastructure for parallelized lab robotic experiments on microtiter plates. Various analytical methods are available for online and at-line measurements. These are combined with advanced digital technologies for data analysis, modeling, experimental design and process optimization. ## Your Job Our Modeling and Simulation Group offers an interdisciplinary and agile research environment within a dynamic and diverse group. The project is an excellent example for research at the interface of computational systems biology and mathematics/statistics with a strong attitude to open research software development. For more information visit http://www.fz-juelich.de/ibg/ibg-1/modsim or http://github.com/modsim. Quantifying the activity of enzymes operating within the large-scale biochemical network is a fundamental challenge in Systems Bio(tech)nology. Here, the unknown parameters must be inferred from models that are incomplete and data that involve errors. For such challenges, Bayesian analysis using Markov Chain Monte Carlo (MCMC) has become the gold standard. For addressing high dimensional parameter inference problems with Bayesian statistics, powerful MCMC methods have been proposed, for example the MCMC differential evolution and the Riemann Manifold Langevin Monte Carlo methods. Because of the specific structure of the inference problems occurring in metabolic models, direct application of these MCMC algorithms is, however, not possible. - In this project, you will bring MCMC methods into the setting of metabolic flux inference and, with inspiration from existing algorithms, develop tailored MCMC algorithms. - You will implement the ensuing algorithms in an existing C++ framework, validate and benchmark them with a realistic case study. - The main focus of the project can develop either more in the mathematical theory of MCMC, the implementation of code for the Jülich supercomputers (GPU/CPU), or being combined with a practical modeling project. ## Your Profile - You are currently enrolled in a Masters degree and are planning your thesis - You are highly motivated, with an interest in probability theory, mathematics, and data science. - Very good practical C++ and Python programming skills allow you to make your ideas happen. - Very good command of written and spoken English (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements ), ideally supported by a certificate - You have strong interest in curiosity-driven multidisciplinary research. ## Our Benefits for You We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! We support you in your work with: - **Meaningful tasks:** Your thesis deals with a future-oriented, socially relevant topic with direct practical relevance in an international environment - **Practical relevance:** With us, you will gain valuable practical experience alongside your studies and actively participate in interdisciplinary projects - **Scientific environment:** You can expect excellent scientific equipment, modern technologies, and qualified support from experienced colleagues - **Personal responsibility:** You organize your tasks independently—from preparation to implementation - **Onboarding & teamwork:** You can look forward to working in a dedicated, international, and collegial team. It is important to us that you quickly settle into the team and are given structured training for your tasks. We also support you from the very beginning and make your start easier with our [Welcome Days and Welcome Guide](https://go.fzj.de/welcome) - **Work-life balance:** We offer flexible working hours to help you balance your professional and personal life. You also have the option of flexible working (in terms of location), which is generally possible after consultation and in line with upcoming tasks and (on-site) appointments - **Flexibility:** Flexible working hours make it easier for you to balance work and study - **Campus experience:** Our research campus in the countryside creates ideal conditions for collegial exchange and sporting activities right on site. Our cafeteria offers a wide range of options—you can enjoy a relaxing lunch break with a lake view - **Fair remuneration:** We will pay you a reasonable remuneration for your thesis In addition to exciting tasks and a collegial working environment, we offer you much more: [https://go.fzj.de/benefits](https://go.fzj.de/benefits) We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us. The following links provide further information on diversity and equal opportunities: [https://go.fzj.de/equality](https://go.fzj.de/equality) and on specific support options: [https://go.fzj.de/womens-job-journey](https://go.fzj.de/womens-job-journey) **Place of Employment:** Jülich **Start Date:** To the next possible date **Salary:** We will pay you a appropriate remunerationfor your thesis **Application Deadline:** The position is advertised until it is successfully filled. [Application Process](https://go.fzj.de/ApplicationInformation) [FAQ](https://go.fzj.de/FAQ_eng) [Contact form](mailto:karriere@fz-juelich.de?subject=Query%20about%202026M-0560%20Master%20Thesis%20-%20Efficient%20Markov%20Chain%20Monte%20Carlo%20Techniques%20for%20Studying%20Large-scale%20Metabolic%20Models) [Career portal](https://go.fzj.de/Careers) [Forschungszentrum Jülich GmbH](https://www.fz-juelich.de/en) [Legal Notice](https://cs-media.b-ite.com/forschungszentrum-juelich/impressum-recruiting-en.pdf)