Internship in EEG & fNIRS Data Acquisition and (Pre-)Processing (f/m/x)

Motivation for 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.

Your Role

Development of an efficient and reproducible workflow for the acquisition and preprocessing of EEG (electroencephalography) and fNIRS (functional near-infrared spectroscopy) data 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 We Offer A dynamic and interdisciplinary research environment 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 and the opportunity to continue your research as part of a master's thesis

Your Profile

Enrolled in a bachelor’s or master’s degree program in biomedical/ electrical engineering, neuroscience, computer science, AI, or related fields Strong programming skills in Python and NumPy Solid understanding of electrical engineering principles Basic 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 Ability to work both independently and 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 Sounds exciting?

Then become part of #teamZEISS and help us shape the future! Please provide your complete application documents (CV, transcript of records, etc.). Your ZEISS Recruiting Team: Selina Safradin