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In a spacious modern setting full of opportunities for further development, ZEISS employees work in a place where expert knowledge and team spirit reign supreme. All of this is supported by a special ownership structure and the long-term goal of the Carl Zeiss Foundation: to bring science and society into the future together.

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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

• ​​​​​​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