Research Associate (f/m/d) Research focus: Disruptive aerodynamic design approaches, HAP3

Modern // regional // industry-oriented: The Brandenburg University of Applied Sciences (Technische Hochschule Brandenburg, THB) is a young and dynamic university with approximately 3,200 students enrolled in 26 study programs across the Departments of Computer Science & Media, Engineering, and Business Administration, located in the city of Brandenburg an der Havel — directly at the doorstep of Potsdam and Berlin. As a family-friendly university on a green campus, we offer attractive working conditions.

Within the Department of Engineering, as part of the research project DaHKIZ (Data-based Holistic AI-supported Civil Engine Development), the following position with the opportunity to pursue a doctoral degree is to be filled at the earliest possible date, initially limited until 30 April 2029: in cooperation with Rolls-Royce Dtl.

Research Associate (f/m/d) Research focus: Disruptive aerodynamic design approaches, HAP3 You work in the field of disruptive design approaches on the investigation of new methods for the evalua-tion of engine components. As classical designs for intake, nacelle and nozzle increasingly reach physical limits, disruptive concepts (e.g. active flow control, morphing geometries) shall be investigated. Your fo-cus lies on the development and application of AI/ML methods to assess the potential of these approaches quickly, in a data-based and reliable manner.

Your main responsibility

• Method development: you compare and decide on methods of Machine Learning (ML) and Artificial Intelligence (AI), such as Variational Autoencoders or GANs, to efficiently analyze high-dimensional design spaces even with sparse data.
• Simulation setup: you create parametric geometries and build simulation chains as "virtual test rigs" for components such as intake, nacelle, bypass duct as well as nozzle and mixer.
• Implementation & training: you prototype selected AI/ML models (e.g. PINNs, graph-based models) within the simulation environment and perform training using large data sets.
• Scientific verification: you systematically verify the developed models with respect to robustness, physical limits, sensitivities and uncertainty quantification.