Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Chalmers University of Technology
- Indiana University
- Aalborg University
- Aalto University
- Forschungszentrum Jülich
- University of Oslo
- AALTO UNIVERSITY
- Australian National University
- Institute for Basic Science
- Mathjobs.org
- OsloMet
- Roskilde University
- Sandia National Laboratories
- SciLifeLab
- University of Copenhagen
- University of Southern Denmark
- Universität Stuttgart
- 7 more »
- « less
-
Field
-
PhD Stipend in Digital Twin Modelling and Multi-Timescale Optimization of Electrolysis Systems Apply Vacant position PhD Stipend in Digital Twin Modelling and Multi-Timescale Optimization of
-
sustainability-driven optimization of hybrid multi-material structures. The work focuses on bringing environmental and circularity goals into structural design alongside strength, stiffness, and cost
-
renewable energy. You will investigate quantum and hybrid quantum–classical methods for power system analysis and optimization, with emphasis on their practical performance and suitability for real
-
neural networks, and physics-informed machine learning in power systems Set up benchmark grids and generate or process suitable simulation data for power-flow, voltage-prediction, or state-estimation
-
Mathjobs.org | about 1 month ago
consists of researchers from various countries and the work is done in English. DIMAG is co-located with the IBS Extremal Combinatorics and Probability Group (ECOPRO). Website: https://dimag.ibs.re.kr
-
consists of researchers from various countries and the work is done in English. DIMAG is co-located with the IBS Extremal Combinatorics and Probability Group (ECOPRO). Website: https://dimag.ibs.re.kr
-
-silicon validation; test generation and optimization; robust device tuning; system-level test; lifetime test and reliability management; and test automation. Further information can be found under
-
successful candidates will develop fundamental theory and practical algorithms that move reinforcement learning closer to real-world systems. Position 1 will focus on trajectory-centric optimization
-
theory and practical algorithms that move reinforcement learning closer to real-world systems. Position 1 will focus on trajectory-centric optimization, Position 2 on adaptive policies, and both will
-
and recovery. Use advanced analytical techniques to identify reaction products and guide process optimization. Collaborate with modelling, reactor engineering, and sustainability experts across the