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Forschungszentrum Jülich in Jülich, Germany, invites applications for a Masters thesis project in computational systems biology and mathematics. The role focuses on Bayesian inference for metabolic networks using MCMC methods and tailored algorithms.
You will implement in C++ within an existing framework, validate against a realistic case study, and contribute to open research software development. The project offers an interdisciplinary and agile research environment, with opportunities to
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.
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 reasonable remuneration for your thesis
Application Deadline: The position is advertised until it is successfully filled.