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

Forschungszentrum Jülich; Jülich; Vor Ort; EUR 40.000 - 60.000; Vollzeit

Zusammenfassung

Das Forschungszentrum Jülich sucht einen Masteranden für eine Masterarbeit über effiziente Markov-Ketten-Monte-Carlo-Techniken zur Untersuchung großangelegter metabolischer Modelle. Der Kandidat wird in einem interdisziplinären Umfeld arbeiten und ist verantwortlich für die Entwicklung und Implementierung innovativer Algorithmen in C++.

Leistungen

Flexibles Arbeiten

Eingehende Betreuung und Unterstützung

Exzellente technische Ausstattung

Interdisziplinäre Zusammenarbeit

Qualifikationen

• Hochmotiviert mit Interesse an Wahrscheinlichkeitstheorien und Datenwissenschaft.
• Sehr gute praktische Kenntnisse in C++ und Python.
• Starkes Interesse an multidisziplinärer Forschung.

Aufgaben

• Entwicklung maßgeschneiderter MCMC-Algorithmen für metabolische Flussinferenz.
• Implementierung der Algorithmen in ein bestehendes C++-Framework.
• Validierung und Benchmarking mit einer realistischen Fallstudie.

Kenntnisse

C++

Python

Mathematik

Datenwissenschaft

Wahrscheinlichkeitstheorie

Ausbildung

Master-Studium

Jobbeschreibung

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

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Job Reference:

22a4410e29b0

Job Views:

4

Posted:

28.06.2025

Expiry Date:

12.08.2025

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Job Description:

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 or

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 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.
  
• You have strong interest in curiosity-driven multidisciplinary research.

Our Offer:

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:

• An interesting and socially relevant topic for your thesis with future-oriented topics
  
• 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
  

In addition to exciting tasks and a collaborative working atmosphere at Jülich, we have a lot more to offer:

We welcome applications from people with diverse backgrounds, 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.

We look forward to receiving your application. The job will be advertised until the position has been successfully filled. You should therefore submit your application as soon as possible.

Apply now

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 or

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 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.

You have strong interest in curiosity-driven multidisciplinary research.

Our Offer:

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:

An interesting and socially relevant topic for your thesis with future-oriented topics

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

In addition to exciting tasks and a collaborative working atmosphere at Jülich, we have a lot more to offer:

We welcome applications from people with diverse backgrounds, 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.

We look forward to receiving your application. The job will be advertised until the position has been successfully filled. You should therefore submit your application as soon as possible.

Apply now

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