Your Job:
Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use machine learning (ML) along with data from previously solved problem instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In particular, we aim to develop a neural network architecture that will allow us to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design‑space exploration, and on‑line operational optimization of power systems.
Your tasks in detail:
1. Become familiar with our previously developed neural network superstructure for learning iterative algorithms
2. Extend the superstructure to tackle AC-PF problems of different complexities and assess its convergence in inference
3. Investigate scaling and performance bottlenecks
4. Explore hybrid ML-classical approaches, the application of meta learning, and the integration of convex optimization layers
5. Increase inference efficiency (, GPU acceleration) and assess the applicability domain of learned algorithms
6. Publish and present your results in peer-reviewed journals and at international conferences
7. Supervise student theses
Your Profile:
8. Excellent Master`s degree with a strong academic background in computational engineering, mathematics, computer science, physics, engineering or a related field
9. Strong background in numerical methods and machine learning
10. Proficiency in at least one programming language (Python, Julia, C++, …)
11. Good analytical skills
12. Good organizational skills and ability to work both independently and collaboratively
13. Effective communication skills and an interest in contributing to an international and interdisciplinary team
14. Working proficiency in English for daily communication and professional contexts
Our Offer:
We offer ideal conditions for you to complete your doctoral degree:
15. Pursue a doctoral degree at RWTH Aachen University (Faculty for Mechanical Engineering) under the supervision of Prof. Alexander Mitsos
16. Excellent scientific and technical infrastructure
17. A highly motivated group as well as an international and interdisciplinary working environment at one of Europe’s largest research establishments
18. Continuous scientific mentoring by your scientific advisors (Prof. Alexander Mitsos, Prof. Uwe Naumann, Dr. Manuel Dahmen)
19. Participate in international conferences
20. Unique HDS-LEE graduate school program (including data science courses, soft skill courses and annual retreats)
21. Further development of your personal strengths,, via a comprehensive further training program; a structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors:
22. 30 Days of annual leave and flexible working arrangements, including partial remote work
23. Targeted services for international employees,, through our International Advisory Service
The position is limited to three years, with a possible one-year extension. Pay is in line with 75% of pay group 13 of the Collective Agreement for the Public Service (TVöD-Bund) and additionally 60 % of a monthly salary as special payment („Christmas bonus“). The monthly salaries in euro can be found on the BMI website: