Master's Student (m/f/d) / Internship - Design and Development of an Analytical Data Pipeline

16.10.2026, Studentische Hilfskräfte, Praktikantenstellen, Studienarbeiten

The Process Analytical Technology (PAT) team of the Chair of Analytical Chemistry and Water Chemistry is looking for Master's thesis or intern to design and develop a data pipeline for bioprocess monitoring. Solid programming/data engineering experience is required. The existing repository has been written in R.

Bioprocesses use microorganisms such as bacteria or fungi to turn simple raw materials like glucose into valuable products, including pharmaceuticals, biopolymers, and green fuels. They are a key technology for sustainable industrial production, but they are chemically complex and often poorly understood. Routine analysis still relies mostly on manual offline sampling, which is labor-intensive and introduces errors and delays.
Raman spectroscopy offers an alternative: it measures directly in the bioreactor (inline) and in real time, enabling automated process monitoring and control. Turning raw spectra into reliable results, however, requires a chain of data-processing steps. In our Lasers and Particles research Group at the Chair for Analytical Chemistry and Water Chemistry at TUM, partnered with the Chair for Bioprocessing Engineering in Straubing, we run fermentations with several model organisms and generate large amounts of process data. Our goal is to accelerate bioprocess development, save resources, and increase product yield through automated monitoring and control.

At the core of our work is an analytical pipeline in R 4.6.0. It is less than a year old and currently hosted on the institute's GitLab (gitlab.lrz.de). Your job is to convert it to a publishable package, improve the codes modularity, runtime and user-friendlyness, and extend its functionality. Python can be used where it is clearly justified, as long as it integrates into the R pipeline and preserves backward compatibility.

Your task in the project:
• Get an overview of the pre-existing code, its functionalities, and the current use case
• Further develop package management using renv, fusen, targets, Roxygen2
• Restructure the code while preserving functionality and enabling future modularity
• Implement data-processing and preprocessing algorithms while optimizing run-time
• Create a coherent package on gitlab
• Implement initial user feedback
• Optionally, experiment with a UI for drag-and-drop analysis
• Your own ideas

What you bring:
• Programming experience, ideally in R. Prior knowledge in data engineering is beneficial
• Familiarity with Git and interest in clean, maintainable software
• Interest in data analysis. No prior knowledge of chemistry or spectroscopy is required

What you gain:
• Experience with software practices: package development, reproducibility, testing, and open-source release
• A public GitHub project that others will use, and that you can show in your portfolio
• Insight into a real-world application at the interface of computer science, process analytical technology, and biotechnology
• Supervision by and insight into a wildly interdisciplinary group

Start:
As soon as possible.

Location:
Our team is located at Georg-Heberer-Straße 11 in Großhadern, Munich. Office space is available, although working remotely may be more convenient for you.

Corresponding lectures:

Current Research in the Instrumental Analysis of Trace Components 1 (CH3122/NAT0170)
Analytische Chemie (CH0107, 2 SWS)
Fortgeschrittene analytische Verfahren (CH4115, 4 SWS)

Organisation:
Simply contact Florian Gollinger (florian.gollinger@tum.de) or Prof. Dr. Christoph Haisch (haisch@tum.de) via email.

Kontakt: florian.gollinger@tum.de