Job Description:

In order to support our way for a sustainable and efficient manufacturing system, CTC Gmb H is looking for an

Intern within “Development of machine learning approaches for industrial energy prediction” (d/f/m)

Are you looking for an internship and want to get to know the work of a Machine learning Engineer? Then apply now! We look forward to you supporting us in Manufacturing Technogies as an intern (d/f/m)!

• Location: Stade
  
• Start: As soon as possible
  
• Duration: 3 - 6 months
  

Your location

We, the CTC Gmb H in Stade, are looking for students for an internship followed potentially by a bachelor or master thesis. The CTC Gmb H is a 100% subsidiary of Airbus Operations Gmb H. It is Germany's leading centre for research and development in the field of design and production of large and complex CFRP components; with an international reputation as an expert for process development, series readiness and production optimisation.

Your benefits

• Work-life balance with a 35-hour week (flexitime).
  
• A final thesis is possible after consultation with the department.
  
• A highly innovative, young environment in a small, dynamic company
  
• Integration into our team with exciting, innovative topics
  
• Direct contact and exchange with the scientific community
  
• Open working atmosphere with flat hierarchies
  
• Freedom to contribute your own ideas
  
• International environment with the opportunity to network globally.
  
• Work with modern/diversified technologies.
  
• At Airbus, we see you as a valuable team member and you are not hired to brew coffee, instead you are in close contact with the interfaces and are part of our weekly team meetings.
  
• Opportunity to participate in the Generation Airbus Community to expand your own network.
  

Your tasks and responsibilities

• Literature research on relevant topics and a requirements analysis
  
• Development of approaches for process simulation with ML
  
• Implementing new concepts
  
• Validation of different machine learning algorithms and approaches
  
• Work on the model definition, training and validation
  
• Hand on work on the implementation of energy prediction with users from Airbus and the CTC
  
• Supporting sustainability in aviation production with holistic simulation approaches
  

Desired skills and qualifications

• Enrolled full-time student (d/f/m) in the area of IT, Engineering or an equivalent field of study.
  
• First practical experience in the field of machine learning is desirable.
  
• Good knowledge in programming (Python e.g.) and machine learning
  
• Experiences with literature review
  
• Fluent in German and/or English.
  
• Ability to work independently and on your own responsibility
  
• Intrinsic motivation and interest in improving sustainability in production
  
• High intrinsic motivation, teamwork and communication skills
  
• Entrepreneurial thinking and acting
  
• Willingness to pass on own competences in a team and to exchange skills with each other
  

Please upload the following documents: cover letter, CV, relevant transcripts, enrollment certificate.

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

Take your career to a new level and apply online now!

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:
CTC Gmb H
Employment Type:
Internship-

Experience Level:
Student
Job Family:

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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.