Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Delft University of Technology (TU Delft); yesterday published
- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
- Delft University of Technology (TU Delft); Delft
- Leiden University
- Amsterdam UMC
- CWI
- Leiden University; Leiden
- AMOLF
- Delft University of Technology
- Delft University of Technology (TU Delft); today published
- Eindhoven University of Technology (TU/e); Eindhoven
- Eindhoven University of Technology (TU/e); today published
- Elestor BV
- Erasmus University Rotterdam
- KNAW
- Maastricht University (UM)
- Nature Careers
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); Amsterdam
- University of Amsterdam (UvA); 10 Oct ’25 published
- University of Twente
- VU Amsterdam
- Wageningen University and Research Center
- 14 more »
- « less
-
Field
-
control systems. It will address practical limitations that prevent reaching theoretical performance, with particular emphasis on optimal feedback design, actuator optimization, and novel control strategies
-
that can self-learn bulk visco-elastic properties? How to structure such materials to learn continually and counteract the aging of their own parts? Can we optimize self-learning materials to achieve
-
? No Offer Description Ageing infrastructures, urbanization, and climate change are intensifying the vulnerability of critical infrastructures (CI), high-tech industries (HTI), and communities
-
first focus on hedging decisions with respect to the uncertainty on the battery model itself. To this end, you will explore concepts such as distributionally robust chance constrained optimization. Second
-
manufacturing and optimization of advanced PTL/MPL architectures, with the goal of improving efficiency, durability, and reducing precious-metal requirements. This is a unique opportunity to combine modeling
-
through optimization of ion channels incorporation and activity in lipid bilayers. The project sits at the interface of biophysics, engineering and biochemistry. The PhD student will be part of
-
distributionally robust chance constrained optimization. Second, you will explore how data-driven models capturing the state-of-health and degradation can be integrated in the battery model. You will develop
-
to the timing of events and the results from performance optimization need to be included in the models. This PhD position will address these challenges by developing new timing-aware distributed supervisory
-
those in a data- and compute-efficient manner to various problem settings. However, the perceptual possibilities are critically hindered by the language-focussed optimization of current foundation models
-
are critically hindered by the language-focussed optimization of current foundation models, fundamentally limiting their ability for spatial reasoning, temporal logic, and operating in low-resource scenarios