JOB DESCRIPTION:

In order to support the Avionics System Design Team of Airbus Military Aircraft Engineering, we are looking for a

DESIRED SKILLS AND QUALIFICATIONS

• Educated to Master Degree level (or equivalent) in Electrical Engineering, Mechatronics, Aeronautical Engineering, Computer Science, Informatics, or similar
• Deep interest in cooperative systems, cognitive robotics, autonomy and artificial intelligence
• Experiences in embedded/hardware-near software development, preferable for airborne mission systems
• Good skills in programming, C/C++, Java, ADA, Python, as well as in Linux, GCC and make scripts, preferably already applied to embedded systems
• Ability to perform self-motivated research work
• Interest in international project workYou are a good team player, have excellent communication skills, and can work independently.
• 
  Please upload the following documents: cover letter, CV, relevant transcripts, enrollment certificate.

EXPERIENCE LEVEL:

Student

This position provides you the opportunity to perform research studies for about 3 years in an industrial environment to receive a PhD degree in cooperation with a university. You will be part of the Avionics System Design Team of Airbus Military Aircraft Engineering located in Manching/Germany. The research will focus on cooperating airborne manned/unmanned platforms, specifically addressing cross-platform collaborative functions that are to be implemented in future avionics mission systems of aircrafts. This includes sensors, computing hardware, and system software.

Your work will include:

• Exploration of AI methods to increase efficiency in technical development processes
• Use of machine learning for analysis, prediction, and optimization of technical system behavior
• AI-based support for decision-making in system development and integration
• Integration of intelligent algorithms into model-based development approaches (e.g., MBSE, SysML)
• Evaluation of explainability, robustness, and acceptance of AI-powered engineering tools in safety-critical contexts
• Investigation of hybrid approaches: combining expert knowledge with data-driven methods
• Use of large language models (LLMs) for automated code generation, modeling, and documentation of technical systems