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# Doctoral Candidate for Operation and Optimization of a Chemical Pilot Plant Using AI-Based Process Data Analysis

- **Company:** Technical University of Munich (TUM)
- **Location:** Straubing, Germany
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Machine Learning, Data Processing, Data Analytics
- **Published:** August 19, 2026
- **Apply:** https://www.careerjet.de/jobad/de2010f0bd28100464e8974dd13c101ac7

## About the Role

* You hold an above-average Master's degree in process engineering (chemical engineering, biochemical engineering, or a closely related field)
 * You have strong interest and hands-on experience in experimental work in a chemical laboratory environment as well as solid programming skills beyond basic scripting
 * Prior experience in machine learning is advantageous, but not required
 * You enjoy working in a multidisciplinary and international environment and are a strong team player
 * You have very good English skills and good German language skills
 * You are interested in supervising students and contributing to teaching activities

## Description

The Professorship of Chemical and Thermal Process Engineering at the Technical University of Munich (TUM), Campus Straubing for Biotechnology and Sustainability, is a leading research group in the field of process engineering and sustainable chemical technologies. Our research focuses on the development, operation, and optimization of innovative chemical processes from pilot to industrial scale.
Applicant's project:
In previous work, a novel chemical process has been successfully implemented at pilot scale (8 t/a) in our technical center. As part of your doctoral research, you will take responsibility for the operation of this pilot plant, conduct measurement campaigns, and perform systematic process optimization.
The research focuses on dynamic plant operation, including transitions between operating points, supported by simulation approaches.
A central scientific aspect is the use of extensive process data generated during plant operation. Within a collaborative project, modern machine learning (ML) methods will be developed, trained, and tested based on these real-world datasets-ideally integrated directly into ongoing plant operation.
A key application example is the early and automated detection of operational anomalies using data-driven approaches.
You will benefit from:

 * Extensive preliminary work on the pilot plant (experimental and modeling) *
 * Existing expertise in data-driven process analysis and machine learning *
 * An excellent scientific and technical environment *
 * Close collaboration within an experienced and well-established research team


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