Stellenticket HTWK Leipzig

Independent research on methods of generative learning in the context of the inverse design of Carnot batteries. Numerical experiments with deep learning frameworks, such as PyTorch or TensorFlow, for the simulation of turbulent flows are to be carried out and the simulation results evaluated in terms of their physical suitability. The position involves writing scientific papers on the research question of the project "Inverse aerodynamic design of turbo components for Carnot batteries by means of physics informed networks enhanced by generative learning" and participating in the activities of the DFG priority program SPP2403 "Carnot Batteries: Inverse Design from Markets to Molecules" as part of the project.

• Successfully completed academic university degree (Master, Diploma or equivalent) in Mathematics, Physics or Computer Science
• Programming skills, preferably in Python
• Ability to work in a team and good communication skills are required
• Good command of German and/or English required; willingness, to learn either English or German is expected

Qualifications desired:

• Knowledge in the field of Deep Learning
• Programming experience with Deep Learning frameworks, such as PyTorch or TensorFlow
• Knowledge in the field of machine learning
• As well as knowledge of the physics of chaotic dynamic systems, especially turbulence modelling

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For further information about the position, please contact Prof. Dr. Gottschalk (gottschalk@math.tu-berlin.de).

Please send your application with the reference number and the usual documents (combined in a single pdf file, max. 5 MB) by email to Prof. Dr. Gottschalk (gottschalk@math.tu-berlin.de).

By submitting your application via email you consent to having your data electronically processed and saved. Please note that we do not provide a guaranty for the protection of your personal data when submitted as unprotected file. Please find our data protection notice acc. DSGVO (General Data Protection Regulation) at the TU staff department homepage:
https://www.abt2-t.tu-berlin.de/menue/themen_a_z/datenschutzerklaerung/ or quick access 214041.

To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities.

Technische Universität Berlin - Die Präsidentin - Fakultät II, Institut für Mathematik, FG Mathematische Modellierung von industriellen Lebenszyklen, Prof. Dr. Gottschalk, Sekr. MA 4-5, Str. des 17. Juni 136, 10623 Berlin