Master Internship (m/f/d) – Characterization and evaluation of the electronic-nose sensor array

29.07.2026, Studentische Hilfskräfte, Praktikantenstellen, Studienarbeiten

About the Project:

Respiratory diseases can change the concentrations and composition of volatile organic compounds (VOCs) in exhaled breath. These changes may create characteristic VOC patterns that can be used as potential indicators of disease.

The Enose AI Project uses an electronic-nose consisting of an array of gas sensors to detect these VOC fingerprints, aiming to contribute to the development of portable and non-invasive respiratory health-monitoring technologies and, in the longer term, respiratory monitoring of astronauts during space missions.

Key Responsibilities:

1. Generate controlled VOC concentrations using a permeation-based method.
2. Expose the Enose sensor array to different VOC substances and concentration levels under controlled experimental conditions.
3. Determine the sensitivity, response characteristics, repeatability, and accuracy of the sensor array.
4. Perform basic data processing and visualization of the measured signals.
5. Critically evaluate and discuss experimental outcomes

Ideal Applicants Should Have:

1. Current enrollment in analytical chemistry, chemical engineering, sensor technology, or a related field.
2. Basic laboratory experience, particularly in the safe handling of chemicals and laboratory equipment.
3. Knowledge of chemometrics, gas mixtures, experimental calibration, multivariate data analysis, or sensor-signal analysis is highly advantageous.

What We Offer:

• Hands-on experience with gas-sensor arrays and permeation-based VOC-generation systems.
• Opportunity to participate in Enose R&D.
• Close supervision and structured training.
• Opportunity to contribute to the ongoing research project.
• A supportive and collaborative working atmosphere.

Start:

As soon as possible.

Location:

Our Team is located in Großhadern, Georg-Heberer-Straße 11.

Note:

This position is unpaid, and it is designed for a Master student as a research intern.

Organisation:

Dr. Shanye Yang (shanye.yang@tum.de) and Prof. Dr. Christoph Haisch.

Kontakt: shanye.yang@tum.de