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

Job Description:

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?

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 parts and 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.

Tasks

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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?

Generation:

• 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/TertikasGeneratingPart-AwareEditable3DShapesWithout3DSupervisionCVPR2023paper.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.

Required Skills:

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

You bring

• Knowledge in related fields, such as machine learning, artificial intelligence, computer vision.

• 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.

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.

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