Sort by
Refine Your Search
-
Listed
-
Employer
- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
- Leiden University
- Delft University of Technology (TU Delft); yesterday published
- University of Amsterdam (UvA)
- University of Groningen
- Delft University of Technology (TU Delft); Delft
- Erasmus University Rotterdam
- Leiden University; Leiden
- Maastricht University (UM)
- University of Amsterdam (UvA); Amsterdam
- University of Amsterdam (UvA); Published today
- University of Twente (UT)
- 3 more »
- « less
-
Field
-
, the researcher will develop theory and algorithms for (hybrid) model selection that allows to exploit domain knowledge through interactive learning. For this we will build on the minimum description length (MDL
-
transportation systems may include a fleet autonomous cars, vans, and buses. This PhD position within FlexMobility will focus on the underlaying assignment and routing algorithms for real-time operation of
-
within FlexMobility will focus on the underlaying assignment and routing algorithms for real-time operation of the vehicle fleet and the multi-objective design of the mixed transporation network. Our key
-
Description We are looking for a PhD-candidate interested in topics that lie on the border of optimization by the use of heuristic algorithms and (Explainable) Artificial Intelligence ((X)AI). Specifically, in
-
new generation of perceptual foundation models by contributing advanced perceptual pre-training and fine-tuning algorithms. What you will do You will carry out research and development in the areas
-
-tuning algorithms. What you will do You will carry out research and development in the areas of perceptual foundation models, using advances in deep machine learning and computer vision. The goal is to
-
on the theoretical and algorithmic development of control methods that combine physical modeling and real-time computation. The work will involve deriving reduced-order models, designing controllers that exploit
-
nature. The PhD candidate will focus on the theoretical and algorithmic development of control methods that combine physical modeling and real-time computation. The work will involve deriving reduced-order
-
such as textiles. 2. Proven ability to develop and implement advanced motion-planning algorithms and real-time control schemes, ideally demonstrated through digital-twin simulations and hardware-in-the-loop
-
sufficiently “compact” (i.e. algorithmically small and computationally efficient) to enable incorporation in integrated PED models. The development of these compact models will involve collaboration with several