PhD Position in Physics-informed deep learning for PDE-constrained optimization in the geothermal energy field

17.03.2025, Wissenschaftliches Personal

The Chair of Renewable and Sustainable Energy Systems (ENS) at the Technical University of Munich (TUM) deals with the modeling and optimization of energy systems on different temporal and spatial scales. For our Research Group Applied Optimization we are looking for a Research Associate / PhD: Physics-informed deep learning for PDE-constrained optimization in the geothermal energy field starting immediately, in full-time.

Research topic:
Geothermal energy is one of the key technologies to decarbonize the heating and cooling sector. To enable efficient, reliable and sustainable use of deep and shallow geothermal systems, it is essential to optimize them (borehole locations, sizing, operation, etc.). The resulting optimization problems are so-called PDE-constrained optimization problems, since the physical processes in the subsurface are governed by PDEs. In order to solve these mathematically challenging problems efficiently, new optimization approaches need to be developed. One promising research direction is the use of physics-informed deep learning, such as physics-informed neural networks or deep neural operator networks.

Tasks:

• Work in a team on national collaborative research projects, which deal with the development of optimization methods for geothermal systems.
• You will be responsible for developing solutions in terms of methodology (theory) and implementation (application) of the new deep learning – enhanced optimization approaches.
• Coordination of the cooperation with the project partners
• Publication of results in peer-reviewed journals and presentation at international conferences
• Contribution to the education of students and thus support of our educational mission.

 Requirements:

• Above average Master’s degree in engineering, applied mathematics, physics or computational science
• Strong mathematical skills and interest in developing new mathematical methods
• Good knowledge of mathematical/numerical optimization methods or deep learning methods
• Enthusiasm for challenging mathematical problems and interdisciplinary collaboration
• Relevant experience with programming languages (preferably Python)
• You work independently, in a structured and reliable manner.
• You are communicative, flexible and able to work under pressure.
• Fluency in English language (writing/speaking)
• Applicants who are already pursuing or hold a doctorate will be automatically disqualified  

We offer:

• An interesting and challenging job in an international and dynamic team at TUM's Garching site
• Flexible working hours
• Individual opportunities for further training
• Employment in accordance with the collective wage agreement for the civil service (TV-L E13, 100%) 
• Pursuing a doctorate within the framework of the work at the TUM

Interested?

Then we look forward to receiving your application; please send them by e-mail to jobs.ens@ed.tum.de with “PhD application – DL Optimization” in the subject. Please include the following documents as a single PDF file titled
“PhD_DLOptimization_YourFirstName_FamilyName”: detailed CV, cover letter, full academic transcript (Bachelor and Master). Please do not include any other documents in the PDF file. If you have any further questions, please do not hesitate to contact Mr. Smajil Halilović (smajil.halilovic@tum.de). The position remains open until filled.

Kontakt: smajil.halilovic@tum.de

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