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

ZEISS; Karlsruhe; On-site; EUR 21,000 - 34,000; Part time

Benefits offered by this job

Mentorship

Master's thesis opportunity

Exposure to neural signal processing

Job summary

ZEISS Innovation Hub @ KIT in Karlsruhe seeks a talented student to help develop workflows for EEG and fNIRS data processing, curate large datasets, and implement quality metrics. You will contribute to online/offline bad-recording detection, data preprocessing, and feature extraction for AI/ML models.

Join a cross-disciplinary team and potentially continue your research as part of a master’s thesis. You will work closely with engineers, neuroscientists, and AI researchers, applying deep

Qualifications

• 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 (supervised/unsupervised learning, deep learning) 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, with motivation to work in agile, interdisciplinary teams.

Responsibilities

• Develop an efficient workflow for acquisition and preprocessing of EEG and fNIRS data.
• Implement quantitative metrics to assess and optimize data quality.
• Curate and organize large datasets of stimulus-brain activity pairs for research.
• Establish online and offline methods for detecting and flagging bad recordings using visualization tools.
• Apply preprocessing techniques to increase the signal-to-noise ratio.
• Prepare data pipelines for AI and machine learning models (feature extraction, artifact removal, normalization).
• Collaborate with engineers, neuroscientists, and AI researchers to integrate deep learning approaches into neural decoding.
• Present and discuss research findings in team and department meetings.

Skills

Communication skills

Teamwork

Independent working

Analytical mindset

Education

Bachelor's or Master's student in biomedical/electrical engineering, neuroscience, computer science or AI

Tools

Python

NumPy

Job description

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