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