Summary
The Helmholtz School for Data Science in Life, Earth, and Energy (HDS-LEE) is offering a PhD position that focuses on the application of machine learning to enhance AC power flow computations. This project aims to develop efficient numerical algorithms that are essential for energy systems engineering.
PhD Position in Learning Tailored Iterative Algorithms for Accelerating AC Power Flow Computations, Germany
Designation
PhD Candidate
| Detail | Information |
|---|---|
| Research Area | Energy Systems Engineering, Machine Learning, Numerical Methods |
| Location | Institute of Climate and Energy Systems, Energy Systems Engineering, RWTH Aachen University, Germany |
| Eligibility | Excellent Master’s degree in computational engineering, mathematics, computer science, physics, engineering, or a related field. |
| Qualifications | Strong background in numerical methods and machine learning; proficiency in Python, Julia, or C++; good analytical and organizational skills; effective communication in English. |
Description
The successful candidate will be engaged in research that involves:
- Familiarizing themselves with existing neural network architectures for iterative algorithms.
- Extending these architectures for varied AC power flow (AC-PF) complexities.
- Investigating scaling and performance bottlenecks.
- Exploring hybrid machine learning-classical approaches and integrating convex optimization layers.
- Increasing inference efficiency and assessing the applicability domain of learned algorithms.
- Publishing and presenting research findings in peer-reviewed journals and at international conferences.
Additionally, the role includes supervision of student theses and participation in continuous scientific mentoring.
How to Apply
Interested candidates should submit their applications through the provided contact form on the HDS-LEE website. Note that applications via email will not be accepted.
Last Date to Apply
The job will remain open until filled, so please submit it as soon as possible. Early applications are encouraged.
For further details, please visit the HDS-LEE website and refer to the FAQ section for any additional queries.
