Research Associate (f/m/d) Research focus: AI in the context of holistic turbine design

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 programmes 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: AI in the context of holistic turbine design
The main focus of your work lies in the industrial research of Main Work Package HAP2 – AI in the context of holistic turbine design. You investigate original methods of Machine Learning (ML) and Artificial Intelli-gence (AI) for the multidisciplinary, robustness-oriented optimization (MDO) of high-pressure turbine components. As current approaches are not yet sufficiently robust for industrial use — due to the geomet-ric complexity of turbine blades (gas path and internal cooling channels) — data-driven methods shall enable a paradigm shift in holistic turbine design.

Your main responsibility

Investigation of ML and AI strategies for the MDO of a turbine blade: down-selection and evaluation of original methods (e.g. Kolmogorov-Arnold Networks, bootstrapping ensembles, explainable AI) with sparse data sets., reduced order modeling ROM
Investigation of ML and AI strategies for the dimensionality reduction of heterogeneous design spaces: analysis of methods (e.g. Kernel SVD, autoencoders) for high-dimensional, mixed continuous-discrete design parameter spaces including topological variations.
Investigation of ML and AI strategies for robustness and reliability analysis: direct transformation of uncertainties into deterministic design parameters and data-driven ROM approaches for robustness-based optimisation.
Application & validation: transfer of the investigated strategies to industrially relevant use cases and their integration into automated multidisciplinary simulation processes (geometry, CFD, temperature, stress, service life).
Publication: you prepare scientific publications and present the results at international conferences.

Your profile

Successfully completed academic university degree (Master's or University Diploma) in a relevant discipline (Mechanical Engineering, Aerospace Engineering, Physics, Computer Science or similar).
Sound knowledge of the fundamentals of Machine Learning, as well as of the design of thermally highly loaded components (turbines, heat transfer) or of numerical fluid/structural mechanics (CFD/FEM).
Confident use of Python.
Very good English language skills, both written and spoken (level C1), for the preparation of international publications.

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