Sort by
Refine Your Search
-
Listed
-
Employer
- University of Groningen
- Leiden University
- University of Twente
- University of Twente (UT)
- CWI
- Utrecht University
- Delft University of Technology (TU Delft); Delft
- Eindhoven University of Technology (TU/e)
- Eindhoven University of Technology (TU/e); Eindhoven
- Leiden University; Leiden
- University of Twente (UT); Enschede
- University of Amsterdam (UvA); Amsterdam
- Wageningen University and Research Center
- Delft University of Technology (TU Delft)
- KNAW
- Maastricht University (UM); Maastricht
- University of Amsterdam (UvA)
- Vrije Universiteit Amsterdam (VU)
- Wageningen University & Research
- Erasmus University Rotterdam
- Maastricht University (UM)
- Radboud University
- University of Groningen; Groningen
- AMOLF
- Erasmus MC (University Medical Center Rotterdam)
- Radboud Universiteit
- Tilburg University
- University of Amsterdam
- Utrecht University; Utrecht
- Vrije Universiteit Amsterdam (VU); Amsterdam
- 20 more »
- « less
-
Field
-
embedding graph-based problems, particularly those known to be challenging for classical computing architectures. Some of your responsibilities will include: Design and develop mixed-signal circuits
-
New LHC Triggers’, which is funded by the Dutch Research Council (NWO). The main goal of this project is to develop theory predictions and new searches for dark matter bound states in the ATLAS
-
, turning geodata into new answer maps. We use knowledge graphs to model these transformations and apply AI methods to scale them across large map repositories, enabling users to explore many ways maps can be
-
in thermodynamics, optimization, and control theory. Strong understanding of mathematical modeling, numerical optimization, and/or model predictive control (MPC). Experience working with large-scale
-
, Honda Research Institute Europe, MIT, Samaya, MathWorks and others. network-wide events – several week-long “Science & Skills” summer-schools/workshops that rotate between partners and blend theory
-
partners and blend theory, hackathons and career events. Dedicated supervision team (academic & industrial) ECTS-accredited courses, travel funding to top conferences and continual peer exchange with 13
-
Infomatter group at AMOLF. The SMIP project aims at revolutionising computing by developing adaptive, smart materials that combine memory and learning directly within their structure, uniting theory and
-
adaptation and learning from their experiences. Using a combination of theory, numerical experiments and precision desktop experiments, we will create 3D materials with self-adapting elastic elements
-
partners and blend theory, hackathons and career events. Dedicated supervision team (academic & industrial) ECTS-accredited courses, travel funding to top conferences and continual peer exchange with 13
-
-analytical workflows, turning geodata into new answer maps. We use knowledge graphs to model these transformations and apply AI methods to scale them across large map repositories, enabling users to explore