PhD Scholarships in Neural Networks: The Technical University of Denmark (DTU) is offering two PhD scholarships focused on advanced research in Physics-Informed Neural Networks and Data Spaces for Energy Systems. These positions aim to develop next-generation scientific machine learning tools and enhance power system resilience through innovative research.
PhD Scholarships in Physics-Informed Neural Networks and Data Spaces for Energy Systems at DTU Wind
Designation
PhD Scholarships
Table
| Detail | Information |
|---|---|
| Research Area | Physics-Informed Neural Networks, Energy Data Spaces, Power Systems, Machine Learning |
| Location | Kgs. Lyngby, Denmark |
| Eligibility/Qualification | MSc graduates in engineering, mathematics, computer science, physics, sustainable energy, or related fields. Must have a two-year master’s degree (120 ECTS points) or equivalent. |
| Last Date to Apply | 15 March 2025 |
Description
DTU Wind and Energy Systems is seeking talented, self-motivated, and team-oriented individuals for two PhD positions. One position will develop physics-informed neural networks to accelerate dynamic power system simulations. The other will focus on expanding Digital Twins and Energy Data Spaces for dynamic simulations and machine learning applications in power systems. These scholarships are part of the ERC Starting Grant project “VeriPhIED” and the Horizon Europe project “ODEON,” aiming to contribute to sustainable and efficient energy systems.
Candidates will engage in cutting-edge research, collaborate with industry partners, and assist in teaching activities, thus fostering both academic and professional development.
How to Apply
Interested candidates must submit their online application by filling out the appropriate form on the DTU website, including the following materials in one PDF file:
- A cover letter motivating the application
- Curriculum vitae
- Grade transcripts and BSc/MSc diploma
- A Research Statement (max. 700 words) outlining proposed research topics, relevance, and approach
- Contact details of two references for recommendation letters
The assessment of applications will occur on a rolling basis, so early submission is encouraged.
This scholarship represents an excellent opportunity for those passionate about advancing their careers in the intersection of machine learning and power systems within a dynamic research environment.
