For the Institute of Fluid Dynamics and Ship Theory of the Hamburg University of Technology for the earliest possible date, we are lookingf or a:

RESEARCH ASSOCIATE (m/f/d) / WISSENSCHAFTLICHER MITARBEITER (m/w/d)

No.: 10523WM8

A continuation of the employment is intended in the event of a timely approval of a positively pre-assessed aviation research project.
Applications from outside Germany are very welcome. Applicants holding a non-German university degree should hand in their B.Sc. and M.Sc. transcripts of records as well as the final certificates in both(!) the original AND the English version.

YOUR TASKS
The position is embedded in a prestigious joint academia-industry research project devoted to modeling simulation of aircraft ditching jointly performed by the DLR, TU Braunschweig, IBK Innovation and Hamburg Univ. of Technology. Emphasis is put to research in combined model reduction and machine learning applications of aircraft ditching loads and structural responses using near-to-real-time methods. Applicants should perform research in the following areas

• (1) Efficient model-order reducing approaches to unsteady loads in ditching of aircraft impact problems using momentum theories.
  (2) Introduction of reduced-order structural response models.
  (3) Enhancement of computational methods by unsteady autoencoder strategies that ground on machine learning (CNN-AE, LSTM) and/or deterministic approaches (POD, DMD, Koopman). Examples included should address ditching of aircrafts with H2 propulsion systems.
  

Please provide a small (0,5 pages) related motivation

YOUR PROFILE

• Excellent Master Dregree in Mechanical Engineering, preferably Theoretical Mechanical Engineering, Industrial/Applied Mathematics, Computational Physics or Scientific Computing
  

• Proven competencies in applied mathematics and fluid dynamics. Focus on computational fluid/structure mechanics using finite-volume/element methods. Knowledge of flow physics modeling and potential flow theories
  
• Background and knowledge of High-Performance Computing and machine learning methods; sound background in physics of fluids; proven background in different programming paradigms. Experience in the use of HPC-systems, coding competencies in TensorFlow/Keras with Python, FORTRAN, C++.
  
• Experience in engineering applications of machine learning and reduced order modeling, industrial CFD and hydrodynamics is an asset.
• Excellent communication as well as proven language capabilities and team working skills
  

OUR OFFER

• Outside of duties, further scientific training is possible, the results can be used for a dissertation (PhD thesis)
  
• Participation in structured education and training programs
  
• Inter-disciplinary/methodological collaborative research at the forefront of science
  
• Transnational/Transdisciplinary working environment and culture
  

For further information please contact Prof. Dr.-Ing. Thomas Rung phone no.: 040 / 428 78 - 6054, Email: thomas.rung@tuhh.de.