Internship Machine Learning (Synthetic Data Generation) (f/m/x)

Welcome to ZEISS – a company that combines innovation and responsibility! Our corporate functions are diverse and make a decisive contribution to the strategic orientation and sustainable success of ZEISS.

We are seeking passionate and talented students who want to make an impact by helping to develop next generation products and innovative machine learning algorithms at ZEISS. Integrated in a fast-growing team of scientists and research engineers, you will work (for at least 3 months (ideally 6), starting as early as possible, preferably already from August or September) on helping us to develop core algorithms and taking academic ideas into working prototypes.

Your Role

With us, you have the opportunity to perfectly combine your studies with practical experience while actively contributing to exciting projects. This allows you to gain valuable skills, your network, and grow both professionally and personally.

Train models for 2D / 3D synthetic data generation

Read latest research on the topic, implement and use SOTA training techniques

Evaluate results qualitatively, come up with metrics to do quantitative evaluations

Plan and conduct experiments, benchmark and analyze results

Document your findings

Your Profile

Bachelor’s degree in Computer Science, Mathematics, Physics, or related fields with an outstanding study record, and currently enrolled in a master’s program

Solid understanding of Machine Learning and Computer Vision

Solid understanding of Flow Matching

Programming background in Python and hands-on experience with Pytorch

Effective communication and presentation skills in English

Ways to stand out:

Industrial / Academic experience with Flow / Diffusion Models

Solid understanding of modern Reinforcement Learning for Image / Video Generation / LLMs

Sounds exciting? Then become part of #teamZEISS and help us shape the future! Please provide your complete application documents (CV, transcript of records).

Your ZEISS Recruiting Team:

Ines Kloda