Research Associate (f/m/d) Research focus: AI in the context of holistic turbine design, Reference number: HAP2

Organisation/Company Technische Hochschule Brandenburg Department HR Research Field Physics » Other Information science » Other Technology » Energy technology Researcher Profile Established Researcher (R3) Positions Master Positions Application Deadline 22 Oct 2026 - 23:57 (Europe/Berlin) Country Germany Type of Contract Temporary Job Status Full-time Hours Per Week 40 Offer Starting Date 1 Dec 2026 Is the job funded through the EU Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

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

Salary group: E 13 TV-L

40 hours/week

Reference number: HAP2

Your area of work:

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 Intelligence (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 geometric complexity of turbine blades (gas path and internal cooling channels) — data-driven methods shall enable a paradigm shift in holistic turbine design.

Your main tasks include:

• 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.

Access and application requirements:

• 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.

In addition, the following would be welcome:

• Practical experience with deep-learning frameworks (e.g. PyTorch, TensorFlow) and knowledge of modern architectures such as Variational Autoencoders, Kolmogorov-Arnold Networks or explainable AI.
• Experience in multidisciplinary optimisation (MDO), in the parametrisation of complex turbine geometries as well as in the automation of simulation processes.
• Knowledge of robustness and sensitivity analysis as well as of uncertainty quantification (UQ).
• Good German language skills (level B2) are desirable; for international applicants, we support the acquisition of these skills within the first 18 months.

Our offer to you:

THB is a reliable employer that supports its staff with a wide range of benefits and services:

• company pension scheme, annual special payment and capital-forming benefits
• good public transport connections, monthly subsidy for the public transport job ticket
• university canteen directly on campus
• attractive further education and training opportunities
• corporate health management
• flexible working hours, 30 days of annual leave (based on a 5-day week), day off on 24 December and 31 December
• use of the university library
• Family and Social Services (compatibility of work and family life)
• long-term, secure personal and professional prospects at a modern workplace