Project description and research environment
The Professorship of Multiscale Modeling of Fluid Materials is part of the Department of Mechanical Engineering at TUM. The research efforts of the group are focused on multiscale simulations, where different models or methods are merged. These state-of-the-art simulation approaches are essential for phenomena where macroscopic fluid dynamics description does not sufficiently describe the system but at the same time, they are too computationally demanding to be simulated purely on the nanoscale. Such situations are encountered across many scientific fields ranging from life sciences to engineering. The successful applicant will work to develop the next generation multiscale coupling schemes, integrate them with emerging machine learning techniques, and apply them to several multiscale phenomena.
The position is open to candidates having M.Sc. or equivalent degree in physics, engineering, applied mathematics or related fields. The following qualifications are highly beneficial but not as important as the commitment to acquire them:
- strong motivation, scientific curiosity, and commitment to scientific excellence
- fluency in written and oral English language (knowledge of German is not required)
- proficiency in common programming languages (especially C/C++ and python)
- experience in machine learning and related packages (e.g. TensorFlow, PyTorch)
- experience in shell scripting and cluster computing
We offer you to join a young research group with a scientifically stimulating atmosphere and to become part of the TUM, which is one of the top European university. The position will be initially offered for one year but with the full intention of a 3-year extension. Salary will be determined according to the Free State of Bavaria public service wage agreement (TV-L E13 100% ). Teaching duties will be in accordance with TUM regulations. The successful applicant will be enrolled in the TUM Graduate School (https://www.gs.tum.de/en/doctorate-at-tum/).
How to apply?
Applications and questions regarding the position should be sent by e-mail to email@example.com. The application should include (preferably in one single PDF document): a cover letter stating your research experiences and interests, CV, transcript of your grades, and optionally a copy of your M.Sc. Thesis. The positions will be filled as soon as possible and only shortlisted candidates will be notified. Preference will be given to applications received before October 1st, 2019.