Internship in EEG & fNIRS Data Acquisition and (Pre-)Processing

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