Internship in Machine Learning within Engineering (D/F/M)

Job Description:

 

Airbus Operations offers an

 

Internship in Machine Learning within Engineering

Are you looking for a strong entry into the world of machine learning? Then you need data and state-of-the-art algorithms. 

 

We have a lot of qualitative data and we have teamed up with Athena Research Center, one of the most prominent AI Research Centers in Europe, to guide us through the latest 3D ML algorithms.

 

Are you interested?

• Location: Hamburg

• Start: 01.09.2024

• Duration: 3-6 months

Over the years we have gathered thousands of CAD 3D data. Recent advances in machine learning enable us today to explore internal patterns, therefore making reuse of existing partsand the development of new parts easier.

 

Two main challenges come along with this growth in the Design Office. The Retrieval of similar parts and, in times of Generative AI, the Creation of new parts through interpolation in the latent space of existing ones. In this context, we want to test different networks and architectures, e.g. explicit (point clouds) and implicit representations (NeRFs) as well as transformers, found in open source AI models. The latter usually make use of open source datasets, e.g. ShapeNet; we want to test them on our own 3D parts.

 

Background information - Incentive 

 

More precisely we want to find out:

• How long does it take to train different models with our own data/3D parts?

• Which hardware/ amount of GPUs do we need?

 

Retrieval:

• How “good” is a Retrieval out of ~200,000 CAD parts?

• Which parameters can we tweak to get preciser results?

 

3D Reconstruction:

• What kind of control can we expect when generating new parts?

• Which is the most appropriate representation for capturing details of connatural parts? 

  • NeRFs, e.g. https://openaccess.thecvf.com/content/CVPR2023/html/Tertikas_Generating_Part-Aware_Editable_3D_Shapes_Without_3D_Supervision_CVPR_2023_paper.html

  • or Point Clouds, e.g. https://arxiv.org/abs/1706.02413 
     

• Which boundary conditions/ invariants are being kept in the generating process?

 

Throughout the project, Athena Research Center advisors will support us along the way, e.g. by giving us the most robust code for each neural network architecture in question.

 

You are an enrolled student within Informatics, Applied Mathematics, Aerospace Engineering or similar field of study.

 

Your tasks

• Set up an environment with python frameworks and GPUs.

• Preprocess large volumes of CATIA data into different formats.

• Build ML models - with guidance - and train them.

• Work with IT teams to ensure seamless integration into existing systems and processes.

 

You bring

• Knowledge in related fields, such as machine learning, artificial intelligence, computer vision, more specifically in 3D reconstruction or at least point clouds.

• Proficiency in Python, proven experience in PyTorch.

• Experience in TensorFlow and other frameworks would be a plus.

• Excellent written and oral communication skills in English.

 

Then

You will join a very experienced engineering team determined to assist you in answering some new fundamental questions we have in common, in the AI era. You 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.

 

Please upload the following: Cover letter, CV, your GitHub (if available), 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:

Airbus Operations GmbH

 

Employment Type:

Internship

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Experience Level:

Student

 

Job Family:

 

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