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

Technische Hochschule Brandenburg; Deutschland; Vor Ort; EUR 50.000 - 70.000; Vollzeit

Benefits dieser Stelle

Company pension scheme

Public transport subsidy

Canteen on campus

Training opportunities

Health management

Flexible working hours

30 days annual leave

Library access

Family support

Zusammenfassung

Die Technische Hochschule Brandenburg sucht eine Forschungsmitarbeiterin bzw. einen Forschungsmitarbeiter (m/w/d) im Bereich disruptiver aerodynamischer Designansätze. In Zusammenarbeit mit Rolls-Royce Dtl.

untersuchen Sie KI/ML-Verfahren zur Bewertung neuer Konzepte für Triebwerkskomponenten (Ansaugung, Nacelle, Düse, Mixer) und entwickeln virtuelle Testsysteme. Eine Promotion wird angestrebt. Voraussetzung ist ein abgeschlossenes Studium (Master oder Diplom) in einem relevanten Fachgebiet,

Qualifikationen

• Abgeschlossenes Hochschulstudium in relevanter Fachrichtung.
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Aufgaben

• Methodenvergleich und Auswahl von ML/AI-Methoden wie VAEs oder GANs.

Kenntnisse

Machine Learning fundamentals

CFD / Aerodynamics

Python proficiency

Ausbildung

Masterabschluss oder Diplom

Tools

PyTorch

TensorFlow

PINNs

Graph-based models

Jobbeschreibung

Organisation/Company Technische Hochschule Brandenburg Department HR Research Field Physics » Other Engineering » Industrial engineering Information science » Other Researcher Profile Established Researcher (R3) Positions Master Positions Application Deadline 22 Oct 2026 - 23:58 (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 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

Salary group: E 13 TV-L

40 hours/week

Reference number: HAP3

Your area of work:

You work in the field of disruptive design approaches on the investigation of new methods for the evaluation 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 focus 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 tasks include:

• 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.
• 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 (Aerospace Engineering, Mechanical Engineering, Physics, Computer Science or similar).
• Sound knowledge of the fundamentals of Machine Learning, as well as of numerical fluid mechanics (CFD) or aerodynamics.
• Confident use of Python or equivalent
• 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 PINNs or graph-based models.
• Experience in the aerodynamic design of engine components (intake, nacelle, nozzle) as well as in the parametrization of complex geometries.
• Knowledge of sensitivity analysis and 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

Additional Information

• 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

Research Field Physics » OtherJuridical sciences » OtherEngineering » Aerospace engineering Years of Research Experience 4 - 10