Brandenburg University of Technology
1 week ago
PhD Opportunity in Disruptive Aerodynamic Design Approaches and AI/ML for Engine Components Brandenburg University of Applied Sciences in Germany
Degree Level
PhD
Field of study
Computer Science
Funding
Research Associate position with the opportunity to pursue a doctoral degree. Salary group E 13 TV-L, 40 hours/week, initially limited until 30 April 2029. No stipend amount is stated; employment benefits include pension scheme, special payment, job ticket subsidy, canteen access, training, health management, and leave.
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) in the Department of Engineering with the opportunity to pursue a doctoral degree. The project is part of DaHKIZ (Data-based Holistic AI-supported Civil Engine Development) and is carried out in cooperation with Rolls-Royce Dtl.
The research focus is disruptive aerodynamic design approaches for engine components. The post looks at new methods for evaluating intake, nacelle, bypass duct, nozzle, and mixer concepts, especially where classical designs reach physical limits. The work combines aerodynamics, numerical fluid mechanics / CFD, and AI/ML to assess disruptive concepts such as active flow control and morphing geometries in a data-based and reliable way.
Main tasks include method development with machine learning and artificial intelligence, simulation setup using parametric geometries and virtual test rigs, implementation and training of AI/ML models such as Variational Autoencoders, GANs, PINNs, and graph-based models, plus scientific verification of robustness, physical limits, sensitivities, and uncertainty quantification. The role also includes preparing publications and presenting results at international conferences.
Eligibility highlights: a completed Master’s degree or university diploma in Aerospace Engineering, Mechanical Engineering, Physics, Computer Science, or a related field; solid knowledge of ML and CFD/aerodynamics; confident Python skills; and very good English at C1 level. German B2 is desirable, and support is offered for international applicants to acquire it within the first 18 months.
Funding/employment: this is a salaried research associate position in salary group E 13 TV-L, 40 hours/week, initially limited until 30 April 2029. The post is not described as a scholarship; it is an employment-based doctoral opportunity.
Application deadline: 22 October 2026. Apply through the university’s online application portal and quote reference number HAP3. For academic questions, contact Prof. Flassig at [email protected]; for application-process support, contact Ms Peters at [email protected].
Funding details
Research Associate position with the opportunity to pursue a doctoral degree. Salary group E 13 TV-L, 40 hours/week, initially limited until 30 April 2029. No stipend amount is stated; employment benefits include pension scheme, special payment, job ticket subsidy, canteen access, training, health management, and leave.
What's required
Applicants must have a completed Master's degree or university diploma in a relevant discipline such as Aerospace Engineering, Mechanical Engineering, Physics, Computer Science, or a similar field. Required skills include sound knowledge of machine learning and numerical fluid mechanics (CFD) or aerodynamics, confident use of Python or equivalent, and very good English language skills at C1 level. Preferred qualifications include practical experience with deep-learning frameworks such as PyTorch or TensorFlow, familiarity with PINNs or graph-based models, experience in aerodynamic design of engine components and geometry parametrization, and knowledge of sensitivity analysis and uncertainty quantification. Good German at B2 level is desirable, though support is offered for international applicants.
How to apply
Apply online via the university’s application portal and quote reference number HAP3. Submit the application by 22.10.2026. For questions about the role, contact Prof. Flassig; for application-process support, contact Ms Peters.
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