Research Associate (f/m/d) Research focus: AI-based design of engine architecture, Reference number: HAP5

Organisation/Company Technische Hochschule Brandenburg Department HR Research Field Physics » Other Engineering » Aerospace 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 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-based design of engine architecture

Salary group: E 13 TV-L

40 hours/week

Reference number: HAP5

Your area of work:

The focus of your work lies in the industrial research: AI-based design of engine architecture. You investigate efficient and physics-informed ML/AI strategies for the preliminary design of engine concepts, in particular the semantic integration of the \"Preliminary Engine Architecture\" tool and the GTlab framework for Large Language Models (LLMs), as well as multi-agent architectures for the automated synthesis and evaluation of design workflows. As customer inquiries for new propulsion concepts require the rapid, integrated evaluation of very large and complex data sets from preliminary design tools, AI methods shall make new technically feasible and requirement-compliant engine concepts quickly accessible.

Your main tasks include:

• AI-supported component selection: investigation and preparation of existing data sets for automated preliminary design, generation of missing data, and provision of a database suitable for ML/AI evaluation.
• AI-supported evaluation and automated analysis of engine concepts: investigation of ontology-based metadata models for the machine-readable description of model structures, workflows and parameter dependencies, as well as a multi-agent architecture (LLMs, Retrieval-Augmented Generation, function calling to GTlab).
• Robust processes in engine design: investigation of data-driven and AI-supported methods for identifying robust starting solutions with high convergence probability, enabling a more efficient preliminary design.
• Application & validation: transfer of the investigated strategies to exemplary use cases in overall engine design and integration of the AI-supported methodology into GTlab.
• 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 (Computer Science, Aerospace Engineering, Mechanical Engineering, Physics or similar).
• Sound knowledge of the fundamentals of Machine Learning, as well as of engine or system design, or of semantic data modelling.
• 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 working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) or multi-agent systems.
• Experience in preliminary engine design, with the GTlab framework or comparable simulation-based design environments, as well as in the conception of software plug-ins.
• Knowledge of ontologies, semantic model representation or machine-readable descriptions of engineering models.
• 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

Research Field Physics » OtherEngineering » Aerospace engineeringInformation science » Other Years of Research Experience 4 - 10

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