The Chair of Thermal Turbomachinery and Aero Engines at the Faculty of Mechanical Engineering is looking for a 

research associate (m,f,x) for 3years with 39,83 hours per week (TV-L E 13)

Priority Programme 2304 Carnot-Batteries: Inverse aerodynamic design of turbo components for Carnot batteries (by means of physics informed networks enhanced) by generative learning

Within the Priority Programme SPP2304 (DFG, German Research Foundation - Priority Programme “Carnot Batteries: Inverse Design from Markets to Molecules” (SPP 2403)), the project “Inverse aerodynamic design of turbo components for Carnot batteries by means of physics informed networks enhanced by generative learning” aims at developing methods from artificial intelligence (AI) to accelerate the inverse design of turbo-machinery components of Carnot batteries. In particular, we will apply generative adversarial networks (GAN) and physics informed neural networks (PINN) to the simulation of turbulent flows with vast variations in the boundary conditions. By the enormous speed-up realizable by AI-driven simulation and design procedures, we endow the inverse design of Carnot batteries with new tools that have the potential to accelerate formerly used unsteady fluid dynamics simulations by a factor of 100 or more. The ideal candidate will work in close cooperation with the research partner TU Berlin specifically on the generation of a virtual validation/training datasets and on the development of a PINNs computation platform, to be extended to encompass the relevant phenomenology of a condensing fluid mixture. The optimal form of the mathematical model, so far only analyzed for very simple physical systems, must be investigated for the construction of an appropriate set of loss functions. Also the appropriate form and composition of the unknown variable vector will have be to identified to guarantee stability and convergence of the solver. The solution of a direct as well as inverse set of problems will be attempted, whereby increasingly complex sets of data (i.e. feature-rich but sparse and/or noisy) will be adopted to train the algorithm. Also the best combination of optimizer and neural network architectures will be assessed. The coarse-grained PINNs solutions thus obtained will be continuously provided to the research partner to establish the envisaged inverse design tool-chain. We look for highly motivated, enthusiastic and highly qualified research assistants, who will carry out the proposed research engaging in numerical investigations with the possibility of obtaining a doctorate.

extent: full-time
duration: temporary
beginning: as soon as possible
application deadline:

Your tasks:

  •  see above

Your profile:

  • MSc/Diploma in the field of Engineering or equivalent (emphasis on numerical/theoretical work, also applied Mathematics);
  • Good acquaintance with the principles and application of Large-Eddy Simulation and other high-fidelity modelling and simulation approaches, with Fluid Mechanics and Turbulence Modelling of reacting flows;
  • Experience (Master-Niveau) with the application of numerical methods for computational fluid mechanics on HPC clusters;
  • Mastering of or willingness to learn the german and English languages is desirable;
  • Excellent communication skills and the ability of working in an international, highly dynamic and motivated team are an invaluable asset.

Our offerings:

  • challenging and varied tasks with a high level of personal responsibility
  • team-oriented cooperation in a committed, international and appreciative team
  • possibilities of location-flexible work
  • a discounted job ticket

Additional information:

At the request of the applicant (m,f,x), the staff council may be involved in selection interviews.

If the position is funded by third-party funds the employee has no teaching obligation.

The Ruhr-Universität Bochum is one of Germany’s leading research universities, addressing the whole range of academic disciplines. A highly dynamic setting enables researchers and students to work across the traditional boundaries of academic subjects and faculties. To create knowledge networks within and beyond the university is RUB’s declared aim.

The Ruhr-Universität Bochum stands for diversity and equal opportunities. For this reason, we favour a working environment composed of heterogeneous teams, and seek to promote the careers of individuals who are underrepresented in our respective professional areas. The Ruhr-Universität Bochum expressly requests job applications from women. In areas in which they are underrepresented they will be given preference in the case of equivalent qualifications with male candidates. Applications from individuals with disabilities are most welcome.

Contact details for your application:

Stefanie Reichstein, Phone: +49234 32 24505
Prof. Dr. Francesca di Mare,

Travel expenses for interviews cannot be refunded.

For information on the collection of personal data in the application process see:

We are looking forward to receiving your application with the specification ANR: 1924 including the names and contact details of three referees until 12/06/2023, send by e-mail (only one pdf) to the following address:

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