PhD student (f/m/d) in the field of engineering, applied physics, mathematics or comparable
Bundesanstalt für Materialforschung und -prüfung (BAM)
To strengthen our team in the division 7.7 “Modelling and Simulation” in Berlin-Steglitz, starting 01.03.2022, we are looking for a
PhD student (m/f/d) in the field of engineering, applied physics, mathematics or comparable
Salary group 13 TVöD Temporary contract for 3 years Full-time / suitable as part-time employment
The Bundesanstalt für Materialforschung und -prüfung (BAM) is a materials research organization in Germany. Our mission is to ensure safety in technology and chemistry.
We perform research and testing in materials science, materials engineering and chemistry to improve the safety of products and processes. At BAM we do research that matters.
Our work covers a broad array of topics in the focus areas of energy, infrastructure, environment, materials, and analytical sciences.
We are looking for talented people to join us.
Your responsibilities include:
Physics-based simulation models such as parameterized differential equations allow to simulate complex problems. These models are often generalizable, i.e. applying the models to slightly different problems if often possible. However, the model must include all the relevant physical phenomena. On the other hand, machine
learning approaches such as neural networks or Gaussian processes allow to model any complex functional relation. But they usually require much larger data sets for training and have only a very limited extrapolation quality.
The aim of the project is to couple these two approaches. The global relations are described by a physics-based model whose parameters are identified using Bayesian inference. In addition, local approximations based on machine learning models allow to include additional physical phenomena.
This project is a collaboration between division BAM 7.7 “Modelling and simulation”, BAM S.3 “eScience” and the Technical University Munich integrated into their PhD program. The project is embedded into an international research environment and offers ideal conditions for creative students.
Your responsibilities include:
Development and implementation of numerical solution procedures for partial differential equations (Finite Element Method, implementation into FEniCS)
Parameter estimation using Bayesian inference
Coupling physics-based models with machine learning approaches (e.g. Gaussian processes)
Presentation and publication of the results in research journals as well as on national and international conferences
Interdisciplinary cooperation and transfer with national and international research groups
Very good university degree (diploma / master) in engineering, applied physics, mathematics or comparable
Experiences in technical mechanics and finite element method
Experience in developing FEM-applications
Programming skills (C++, python)
Ideally experience in machine learning applications
Ideally experience with Bayesian inference for parameter estimation
English skills, written and spoken
Good communication and Information behaviour, goal-oriented and structured way of working
Ability to work in a Team / willingness to cooperate, willingness to learn and initiative / commitment
Interdisciplinary research at the interface of politics, economics and society
Work in national and international networks with universities, research institutes and industrial companies
Outstanding facilities and infrastructure
Flexible working hours and mobile working
We welcome applications via the online application form by 09.02.2022. Alternatively, you can also send your application by post, quoting the reference number 11/22-7.7 to:
Bundesanstalt für Materialforschung und -prüfung Referat Z.3 - Personal Unter den Eichen 87 12205 Berlin GERMANY www.bam.de
Dr. Unger will be glad to answer any specific questions you may have. Please get in touch via the telephone number +49 30 8104-3787 and/or by email to Joerg.Unger@bam.de.
BAM pursues the goal of professional equality between women and men. We therefore particularly welcome applications from women. In addition, BAM supports the integration of severely disabled persons and therefore especially welcomes their applications. With regard to the fulfilment of the job advertisement requirements, the application documents are examined individually. Recognised severely disabled persons will be given preferential consideration if they are equally suitable.
The advertised position requires a low level of physical aptitude.