Internship within Engineering (m/f) Aerodynamic Predictions with Machine Learning, Bremen

Airbus pioneers sustainable aerospace for a safe and united world. The Company constantly innovates to provide efficient and technologically-advanced solutions in aerospace, defence, and connected services. In commercial aircraft, Airbus offers modern and fuel-efficient airliners and associated services. Airbus is also a European leader in defence and security and one of the world's leading space businesses. In helicopters, Airbus provides the most efficient civil and military rotorcraft solutions and services worldwide.

Tasks & accountabilities

For a test case where surface pressure information is available from CFD for a limited number of flow parameter variations; intermediate surface pressure solutions shell be approximated.

Different state of the art mathematical approaches shell be implemented and validated to build a multi parameter model to be benchmarked to a POD based approach.
Uncertainty quantification and Supervised Machine Learning will be key elements of the work.

The overall task is structured as follows:

• Study of state of the art approaches
• Implementation and validation
• Benchmark to conventional processes
• Documentation and presentation
• This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth.

Required skills

• Enrolled student within Aerospace Engineering, Mathematics, Computer Science or similar field of study
• Advanced programming skills in Matlab and Python
• User experience with Linux
• English: Fluent