Research Assistant/Internship (m/f/d)

The Institute of Engineering Thermodynamics conducts research with over 150 staff members in the field of efficient, resource-conserving energy storage and next-generation energy conversion technologies. Our work spans from theoretical studies and fundamental laboratory research to the operation of pilot-scale facilities.

What to expect

At the institute of Engineering Thermodynamics, DLR Stuttgart operates a state-of-the-art test facility for the experimental investigation and characterization of high-temperature fuel cell and electrolysis modules from various manufacturers, with electrical output of up to 120 kW. These modules are operated either in fuel cell mode to generate electricity or in electrolysis mode to produce syngas from CO2 and/or steam for energy storage applications. Within multiple research projects, the modules undergo extensive experimental campaigns for product optimization in close cooperation with industrial partners and are typically replaced after several months of operation.

In parallel, the Electrochemical High-Temperature Processes (EHT) group develops models and control strategies to enable operation of these modules under novel conditions, with the overarching goal of supporting the broad application of solid oxide cell (SOC) technologies. Model development is based on an in-house transient simulation tool as well as machine learning methods that leverage experimentally generated data to predict future system behaviour.

As an intern, student assistant or thesis student, you will support the adaptation, optimization and extension of the test facility for new modules and contribute to the development of prediction models for use in subsequent experimental campaigns.

Your tasks

• Practical work on adaptation of test rigs for new modules
• Support during experimental campaigns
• Planning tasks and support for test rig control system development
• Development of prediction models using machine learning methods and existing experimental data
• Use of simulation tools to define and evaluate operating strategies

Your profile

• First hand experience with machine learning methods
• Basic knowledge of fuel cell and electrolysis technologies
• First hand experience with experimental setups
• Practical and technical skills
• Capacity to work independently and reliable
• Enthusiasm for hands-on and experimental work

We look forward to getting to know you!

If you have any questions about this position (Vacancy-ID 4118) please contact:

Marc Heddrich
Tel.: L-WAHL: +49 711 6862 8184