Vacancy: Student(f/m/d) for master thesis 'Potential of Neural Networks for Image-based One-Shot-Predictions of Aero-Solutions'
Potential of Neural Networks for Image-based One-Shot-Predictions of Aero-Solutions (m/f/d)
The design of aircraft engine compressors and turbines is a long period optimization task which requires many iterations of time consuming CFD (Computational Flow Field) simulations until the resulting aero solutions reach the desired quality standards. In order to speed up those iterations the potential of Convolutional Neural Networks (CNNs) in combination with Recurrent Neural Networks (RNNs) will be evaluated. The objective is to create a concept and to develop and test a prototype for image-based one-shot-predictions of 2D-Aero-solutions.
- Getting acquainted with the problem domain of aero solutions and CFD simulations. Analyzing and becoming familiar with the existing environment and provided CFD data.
- Studying the literature and identifying the most promising procedures to accelerate CFD simulations of internal aerodynamics of aircraft turbines by the capabilities of Deep Learning methods
- Implementing these procedures and checking the performance and accuracy
- Performing tests on various turbine geometries
- Major in computer science, mathematics or physics
- Programming experience in Python or related languages
- First experience and familiarity with the generation of image reconstructions (e.g.
with Autoencoders) would be a plus
- Familiar with PyTorch, PyTorch Geometric, Pandas, Numpy, etc. • Practical experience with Deep Learning (CNN, RNN, etc.)
- Willing to get familiar with existing code
- Duration: 6 months
- Insights into practical work in aviation as an innovative, high-tech industry
- Exciting jobs that carry responsibility and are performed in an atmosphere of team
- A personally assigned contact from company and university side
- Flexible working hours and possibility to work remotely in the home office
- Networking opportunities
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