Research Associates / Doctoral Candidates (f/m/d) – Machine Learning Applications to Sustainable Energy Management

27.02.2023, Wissenschaftliches Personal

The newly established Professorship of Energy Management Technologies at TUM’s School of Engineering and Design is looking for Research Associates / Doctoral Candidates (f/m/d) in Machine Learning Applications to Sustainable Energy Management. You are passionate about applying cutting-edge information technology to solve the energy and climate crisis and would like to work in a vibrant research environment? Then let’s design the energy systems of the future together!

Our research focus:

The researchers working at the newly established Professorship of Energy Management Technologies are focusing on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and prototypical systems controlling complex energy systems like electric grids and thermal systems for a sustainable future. These systems coordinate distributed renewable generation like solar and wind, flexible loads like heat pumps and electric vehicles, and distributed energy storage like stationary batteries and hydrogen storage to maximize energy efficiency while keeping the grid reliable and secure. Our research method is engineering-oriented, prototype-driven, and highly interdisciplinary. Our typical research process includes the evaluation of existing systems, extensive simulation-based analyses, as well as the implementation and validation of algorithm and system designs in real world settings.

Your tasks:

You will work on key research projects within the context described above. The Professorship of Energy Management Technologies closely collaborates with other Professorships at TUM, industry partners, and partner research institutions. You support us in making this cooperation efficient and productive. As Research Associate you will also support our teaching activities in several Bachelor and Master programs offered by the School of Engineering and Design. You help us to prepare teaching material, serve as teaching assistant in our lectures, support lab courses, and supervise student research.

Your profile:

• Above-average master’s degree in computer science, electrical or mechanical engineering, applied mathematics, or a similar engineering-oriented quantitative discipline
• Advanced software development and data analytics skills
• Hands-on mentality with first practical experience in developing innovative technology
• Inquisitive and passionate about research and knowledge transfer
• Independent, creative, and committed way of working
• Ability to think conceptually and analytically
• Basic knowledge of energy technology
• Very good command of English
• Big plus: Good command of German

Our offer:

We offer you the opportunity to do research within a team of highly motivated researchers and benefit from the research environment offered by one of the best universities in Europe and world-wide. We support your doctoral dissertation in the research areas outlined above. The offered positions for Research Associates (pay grade TV-L 13) can be filled starting May 2023 and are initially limited to 2 years. Further employment is possible and intended.

To ensure equal opportunity, applications from qualified women are particularly welcome. Disabled candidates will be given preference if they are equally qualified. Applications from candidates with an international background are explicitly encouraged.

Your application:

We are looking forward to your application until April 15, 2023. Please submit it as one single PDF file via email to applications.emt@ed.tum.de. The application should contain the following documents:

• Curriculum vitae
• Complete academic transcripts
• Letters of reference from previous positions held, including internships
• Bachelor and Master thesis

TUM aims to increase the proportion of women. Qualified women are therefore strongly encouraged to apply. Severely disabled applicants will be given preference if they have essentially the same suitability and qualifications.

If you have any questions prior to applying, please contact Prof. Dr. Christoph Goebel at christoph.goebel@tum.de.

Kontakt: christoph.goebel@tum.de