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

Motivation for the WorkTurning 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 RoleDevelopment of an efficient and reproducible workflow for the acquisition and preprocessing of EEG (electroencephalography) and fNIRS (functional near-infrared spectroscopy) dataImplement quantitative metrics to assess and optimize data qualityCurate and organize large datasets of stimulus-brain activity pairs for research applicationsEstablish online and offline methods for detecting and flagging bad recordings using visualization toolsApply and evaluate advanced preprocessing techniques to increase the signal-to-noise ratioPrepare 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 decodingPresent and discuss research findings in team and department meetingsWe OfferA dynamic and interdisciplinary research environmentExposure to state-of-the-art methods in neural signal processing and data curationOpportunity to contribute to AI-ready datasets for machine learning applications for neural decodingClose mentorship and the opportunity to continue your research as part of a master's thesisYour ProfileEnrolled in a bachelor’s or master’s degree program in biomedical/ electrical engineering, neuroscience, computer science, AI, or related fieldsStrong programming skills in Python and NumPySolid understanding of electrical engineering principlesBasic knowledge of electrophysiology, neural signal processing, and machine learningExperience with data preprocessing, signal analysis, and feature extraction is highly desirableFamiliarity with AI/ML concepts (e.g., supervised/unsupervised learning, deep learning architectures) is a plusCreative, pragmatic, and self-motivated with strong analytical skillsAbility to work both independently and in a team-oriented environmentExcellent communication skills in English or GermanPassion for innovation and enthusiasm for new technologies as well as motivation to work in agile, interdisciplinary teamsSounds 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