Brandenburg University of Technology
1 week ago
Research Associate PhD Opportunity in AI for Holistic Turbine Design Brandenburg University of Applied Sciences in Germany
Degree Level
PhD
Field of study
Computer Science
Funding
Salary group E 13 TV-L, 40 hours/week. The position is initially limited until 30 April 2029 and includes the opportunity to pursue a doctoral degree. No stipend is mentioned; this is a paid employment position.
Deadline
Oct 22, 2026
Country
Germany
University
Brandenburg University of Technology
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About this position
Brandenburg University of Applied Sciences is advertising a Research Associate (f/m/d) position in the project DaHKIZ (Data-based Holistic AI-supported Civil Engine Development), with the opportunity to pursue a doctoral degree. The research focus is AI in the context of holistic turbine design, carried out in cooperation with Rolls-Royce Dtl.
The work centers on industrial research for high-pressure turbine components, including machine learning and artificial intelligence methods for multidisciplinary, robustness-oriented optimization (MDO). Topics include sparse-data ML/AI methods, reduced-order modeling, dimensionality reduction for complex design spaces, robustness and reliability analysis, uncertainty handling, and integration into automated simulation workflows involving geometry, CFD, temperature, stress, and service-life analysis.
Applicants should hold a completed Master's degree or university diploma in a relevant field such as Mechanical Engineering, Aerospace Engineering, Physics, Computer Science, or a similar discipline. Strong knowledge of ML is expected, along with either turbine/heat-transfer design or numerical fluid/structural mechanics (CFD/FEM). Python skills and very good English (C1) are required. Helpful extras include PyTorch/TensorFlow, explainable AI, Variational Autoencoders, Kolmogorov-Arnold Networks, CAD (preferably Siemens NX), MDO, robustness/sensitivity analysis, and uncertainty quantification. Good German (B2) is desirable, and support is offered for international applicants to acquire it within the first 18 months.
The position is paid under Salary group E 13 TV-L, with 40 hours/week, and is initially limited until 30 April 2029. Applications are due by 2026-10-22 via the online application portal, quoting reference number HAP2.
Funding details
Salary group E 13 TV-L, 40 hours/week. The position is initially limited until 30 April 2029 and includes the opportunity to pursue a doctoral degree. No stipend is mentioned; this is a paid employment position.
What's required
Applicants should have a completed Master's degree or university diploma in Mechanical Engineering, Aerospace Engineering, Physics, Computer Science or a similar relevant discipline. Required qualifications include sound knowledge of machine learning and either the design of thermally highly loaded components (turbines, heat transfer) or numerical fluid/structural mechanics (CFD/FEM), confident Python skills, and very good English at C1 level. Desirable experience includes PyTorch/TensorFlow, modern architectures such as Variational Autoencoders, Kolmogorov-Arnold Networks or explainable AI, CAD systems preferably Siemens NX, multidisciplinary optimisation, turbine geometry parametrisation, simulation automation, robustness/sensitivity analysis, and uncertainty quantification. Good German at B2 level is desirable, with support for international applicants to acquire it within 18 months.
How to apply
Apply via the university's online application portal and quote reference number HAP2. Submit the application by 22.10.2026. For questions, contact Prof. Flassig or Ms Peters by email.
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