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The Forschungszentrum Jülich GmbH invites applications for a Master Thesis - Efficient Markov Chain Monte Carlo Techniques for Studying Large-scale Metabolic Models in Jülich, Germany. You will join the Modeling and Simulation Group to tackle parameter inference challenges using Bayesian statistics and advanced MCMC methods.
Working with a multidisciplinary team, you will implement and benchmark novel MCMC algorithms in a C++ framework, with potential GPU/CPU optimization.
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.
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.
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:
In addition to exciting tasks and a collegial working environment, we offer you much more: 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 and on specific support options: 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.