Sort by
Refine Your Search
-
Listed
-
Employer
- University of Groningen
- Leiden University
- University of Twente
- Wageningen University and Research Center
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); Delft
- Eindhoven University of Technology (TU/e)
- Eindhoven University of Technology (TU/e); Eindhoven
- Erasmus University Rotterdam
- Leiden University; Leiden
- Radboud University
- Radboud University Medical Center (Radboudumc); Nijmegen
- University of Twente (UT)
- University of Twente (UT); Enschede
- Utrecht University
- 5 more »
- « less
-
Field
-
trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with these challenges. By
-
to make viable trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with
-
Vacancies PhD Position in Algorithmic Energy Trading Key takeaways In recent years, the energy sector has undergone changes that have a high impact on the dynamics in power markets. One
-
by the team working on these systems. You will perform measurements of AI algorithms to fill in the unknowns uncovered in such a data flow diagram. The energy scalability of the core algorithms of a
-
Apply now The Faculty of Science, Leiden Institute of Advanced Computer Science, is looking for a: PhD Candidate, Efficient LLM Algorithm, Hardware and System Design (1.0 FTE) Project description We
-
PhD Position in Probabilistic and Differential Algorithms Faculty: Faculty of Science Department: Department of Information and Computing Sciences Hours per week: 36 to 40 Application deadline
-
advantages for manipulation and locomotion, but current control algorithms do not fully exploit their capabilities. Most rely on approximations tailored for rigid systems or require extensive sensing and
-
operate safely around humans. They offer unique advantages for manipulation and locomotion, but current control algorithms do not fully exploit their capabilities. Most rely on approximations tailored
-
Europe, Altair, MIT and 12 other partners to build an AI Partner in Engineering (AIPE) that converses with engineers, proposes designs, explains its reasoning and even writes new optimisation algorithms
-
to ensure high predictive capability and, on the other hand, keeping the models sufficiently “compact” (i.e. algorithmically small and computationally efficient) to enable incorporation in integrated PED