Internship within Aerodynamic Machine Learning (d/f/m)

InternHunt

Hamburg

Hybrid

EUR 7.100 - 9.600

Teilzeit

vor 5 Stunden
Sei unter den ersten Bewerbenden
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Benefits dieser Stelle

35-hour week (flexitime)
International environment
Generation Airbus Community networking

Zusammenfassung

Airbus Operations GmbH in Hamburg invites highly motivated students to undertake a six-month internship in Aerodynamic Machine Learning. You will develop ML methods to compute gust-induced pressure fields and compare with nonlinear aeroelastic results.

The role offers a 35-hour week, a monthly salary of 750 EUR gross, and an international, collaborative environment. The internship is located at the Hamburg site with ferry access from the Elbe and opportunities to engage with multiple internal

Qualifikationen

  • Enrolled full-time student in Engineering, Physics, Maths, Data Science or equivalent.
  • Strong knowledge in Aerodynamics and CFD.
  • Proficient coding in Python and/or Matlab.
  • Experience training machine learning models.
  • Fluency in English; German helpful.

Aufgaben

  • Develop ML-based methods to obtain pressure fields from gust events.
  • Compare ML results with nonlinear aeroelastic predictions.
  • Collaborate with multiple interfaces in an international environment.

Kenntnisse

Aerodynamics
CFD
Python
Matlab
Machine Learning
English (Negotiating Level)

Ausbildung

Enrolled full-time student in Engineering/Physics/Data Science

Jobbeschreibung

Internship within Aerodynamic Machine Learning (d/f/m)

Hamburg, Germany

2026-09-29

Role Description

Job Description: In order to support the Loads & Aeroelastics department, Airbus Operations is offering an

  • Location: Hamburg
  • Start: as soon as possible
  • Duration: 6 months

Are you keen to master the way we compute external loads acting on the vehicle due to maneuvers, flight in turbulence, landing impact, ground-handling?

Do you like to work in an empowered manner?

Do you enjoy connecting people and collaborating with many different interfaces?

This job will give you the opportunity to work internationally with several internal flight physics departments and with teams located in Europe and India.

About UsWe are a team of 17 multicultural and passionate engineers who perform the gust calculations for overall aircraft design for all aircraft types. Our team is involved from a very early design stage until certification and also in-service support. We are responsible for running build model activities such as finite element modeling, creating mass and aerodynamics models. We perform gust loads calculation, including failure cases, and deliver to internal interfaces. On top, we are deeply involved in research and technology aspects with new aircraft configuration assessments, model based system engineering, automation and machine learning.

Your locationYou will be working at the largest production site for civil aircraft situated in Hamburg. Its location on the southern banks of the river Elbe includes the option to commute by ferry. Experience the special flair of Hamburg in your spare time where vibrant cosmopolitan culture meets nautic legacy.

Are you wondering how you actually get from university to Airbus? Your working student position is at the Finkenwerder plant and you can take the ferry from Teufelsbrück to the Airbus ferry terminal and back to the university in the morning and at noon. Alternatively, you can take the ferry to Finkenwerder and then ride your bike for about another 15 minutes to the main entrance.

  • Work-life balance with a 35-hour week (flexitime) and EUR 750 gross/monthly salary.
  • International environment with the opportunity to network globally.
  • Work with modern/diversified technologies.
  • Opportunity to participate in the Generation Airbus Community to expand your own network.

Your Tasks And ResponsibilitiesDuring this six months internship you will develop methods based on machine learning in order to obtain the pressure fields due to a gust event. You will compare the results versus the non-linear results.

  • Optimize the global design of Airbus products during the concept phases
  • Build and develop methods to consider nonlinearities inside an Aeroelastic model
  • Actively support us as a new member of our team on our journey

Desired Skills And Qualifications

  • Enrolled full-time student (d/f/m) in the area of Engineering, Physics, Maths, Data science or an equivalent field of study.
  • You have strong knowledge and experience in Aerodynamics and CFD.
  • You have strong knowledge and experience in coding using Python and/or Matlab.
  • Knowledge and experience with training of machine learning models .
  • You are passionate to understand physical aspects and to use your engineering judgment.
  • Your English is at negotiating level whereas German language skills would be beneficial.

Not a 100% match? No worries! Airbus supports your personal growth.

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.

Company:Airbus Operations GmbH

Employment Type:Internship

Experience Level:Student

Job Family:Flight & Space Physics

By submitting your CV or application you are consenting to Airbus using and storing information about you for monitoring purposes relating to your application or future employment. This information will only be used by Airbus.

Airbus is committed to achieving workforce diversity and creating an inclusive working environment. We welcome all applications irrespective of social and cultural background, age, gender, disability, sexual orientation or religious belief.

Airbus is, and always has been, committed to equal opportunities for all. As such, we will never ask for any type of monetary exchange in the frame of a recruitment process. Any impersonation of Airbus to do so should be reported to emsom@airbus.com.

At Airbus, we support you to work, connect and collaborate more easily and flexibly. Wherever possible, we foster flexible working arrangements to stimulate innovative thinking.

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