Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); yesterday published
- Delft University of Technology (TU Delft); Published yesterday
- University of Amsterdam (UvA)
- Erasmus University Rotterdam
- Delft University of Technology (TU Delft); today published
- Vrije Universiteit Amsterdam (VU)
- Wageningen University & Research
- Amsterdam UMC
- Delft University of Technology (TU Delft); Published today
- Wageningen University and Research Center
- Leiden University
- University of Amsterdam (UvA); Published today
- University of Amsterdam (UvA); yesterday published
- University of Groningen
- University of Twente (UT)
- Utrecht University
- Delft University of Technology (TU Delft); 17 Oct ’25 published
- University of Amsterdam (UvA); today published
- CWI
- Delft University of Technology (TU Delft); 10 Oct ’25 published
- Delft University of Technology (TU Delft); 3 Oct ’25 published
- Eindhoven University of Technology
- Eindhoven University of Technology (TU/e); Published yesterday
- KNAW
- Leiden University; yesterday published
- Maastricht University (UM); yesterday published
- Universiteit van Amsterdam
- University Medical Center Utrecht (UMC Utrecht)
- University Medical Center Utrecht (UMC Utrecht); yesterday published
- University of Amsterdam (UvA); 10 Oct ’25 published
- University of Amsterdam (UvA); Published 14 Nov ’25
- University of Amsterdam (UvA); Published 28 Nov ’25
- University of Amsterdam (UvA); Published yesterday
- Utrecht University; today published
- Vrije Universiteit Amsterdam
- Wageningen University & Research; today published
- 27 more »
- « less
-
Field
-
reasoning to integrate data and theory. Methods include regression analysis, experiments, Bayesian modeling, and abductive reasoning. Our work in this area also covers how firms learn and protect knowledge
-
mobility systems. In our 12 collaborative labs we apply advanced technologies such as sensing, data analytics, modelling, and AI to turn scientific research into real-world impact. You’ll join an open
-
, modelling, and AI to turn scientific research into real-world impact. You’ll join an open, supportive environment that fosters learning and professional growth. Job requirements Must haves: Master’s degree in
-
learning models keep getting better, but how they make their decisions often remains unclear, because these depend on many incomprehensible model parameters that have been learned from data. For instance
-
extra holiday leave hours on an annual basis. For part-timers, this is calculated pro rata. 8% holiday allowance and 8.3% end-of-year bonus contribution to commuting expenses optional model for designing
-
transduction could enable a new class of transistor circuits as well as novel sensor and transducers. Your PhD project will focus on both the fundamental physics of these devices with modelling, fabrication, and
-
to significantly reduce the turnaround time of SRS, thus enabling their use for industrial design processes. By combining state-of-the-art numerical methods and data-driven modelling techniques, the PhD candidate
-
physics of these devices with modelling, fabrication, and testing of these materials. You’ll join our highly motivated, supportive and cohesive team of professors, teachers, technicians, PhD students and
-
methods and data-driven modelling techniques, the PhD candidate will investigate novel frameworks to accelerate SRS in HPC environments. The PhD candidate will work at the Ship Hydrodynamics section
-
Your job Are you a highly motivated and enthusiastic individual with a strong background in process-based modeling, data analysis, and soil sciences? Do you want to participate in a large-scale