Machine learning engineer Jobs in Ettlingen

Motivation of the Work

Turning today’s research into tomorrow’s applications – together. At ZEISS, we focus on user-centric innovation to transform ideas into cutting-edge solutions. The ZEISS Innovation Hub @ KIT fosters collaboration between students, researchers, and industry professionals to drive technological advancements in neuroscience applications.

We are looking for highly motivated students to support the acquisition, quality control, and curation of EEG (Electroencephalography) and fNIRS (functional Near-Infrared Spectroscopy) data. This role offers a unique opportunity to work hands-on with human neuroimaging data, ensuring high-quality recordings and organizing datasets for research in neural decoding and AI-driven analysis.

If you are passionate about neuroscience, and signal processing and eager to contribute to cutting-edge research, join us!

We Offer

A dynamic and interdisciplinary research environment

Hands-on experience with EEG and fNIRS data acquisition and lab equipment

Exposure to state-of-the-art methods in neural signal processing and data curation

Opportunity to contribute to AI-ready datasets for machine learning applications for neural decoding

Close mentorship

An agile work environment

The possibility of continuing as part of a Master’s thesis project

Your Role

Develop an efficient and reproducible workflow for EEG and fNIRS data acquisition and preprocessing

Implement quantitative metrics to assess and optimize data quality

Curate and organize large datasets of stimulus-brain activity pairs for research applications

Establish online and offline methods for detecting and flagging bad recordings using visualization tools

Apply and evaluate advanced preprocessing techniques to increase the signal-to-noise ratio

Prepare data pipelines for AI and machine learning models (feature extraction, artifact removal, and normalization)

Collaborate with a team of engineers, neuroscientists, and AI researchers to integrate deep learning approaches into neural decoding

Present and discuss research findings in team and department meetings

Your Profile

Enrolled in a Bachelor’s or Master’s program in biomedical engineering, electrical engineering, neuroscience, computer science, AI, or related fields

Strong programming skills in Python and NumPy

Solid understanding of electrical engineering principles

Fundamental knowledge of electrophysiology, neural signal processing, and machine learning

Experience with data preprocessing, signal analysis, and feature extraction is highly desirable

Familiarity with AI / ML concepts (e.g., supervised / unsupervised learning, deep learning architectures) is a plus

Creative, pragmatic, and self-motivated with strong analytical skills

Capable of working independently as well as in a team-oriented environment

Excellent communication skills in English or German

Passion for innovation and enthusiasm for new technologies as well as motivation to work in agile, interdisciplinary teams

Your ZEISS Recruiting Team :

Franziska Gansloser