Sort by
Refine Your Search
-
Listed
-
Employer
- University of Groningen
- Utrecht University
- CWI
- European Space Agency
- Radboud University
- Delft University of Technology (TU Delft)
- Erasmus University Rotterdam
- NIOZ Royal Netherlands Institute for Sea Research
- Radboud University Medical Center (Radboudumc)
- University of Amsterdam (UvA)
- University of Twente
- University of Twente (UT)
- Wageningen University and Research Center
- Delft University of Technology (TU Delft); Delft
- Eindhoven University of Technology (TU/e)
- Max Planck Institute (MPI) for Psycholinguistics
- Nature Careers
- Radboud University Medical Center (Radboudumc); Nijmegen
- Tilburg University
- University of Amsterdam (UvA); Amsterdam
- University of Groningen; Groningen
- Vrije Universiteit Amsterdam (VU)
- Vrije Universiteit Amsterdam (VU); Amsterdam
- Wageningen University & Research
- 14 more »
- « less
-
Field
-
in combination with other machine learning techniques, to create predictive models. You will engage in an interactive feedback loop with domain experts to analyze discovered models and remove any
-
related to learning, behavior, and interactions between humans and systems using advanced quantitative methods. You will be part of a team developing and testing VR simulations. You will perform and conduct
-
. An important focus of our research is how these abiotic-biotic interactions create value for society, following for example the “Building with Nature” paradigm or investigating sustainable sources of marine
-
report tackling the milestones with much output there is freedom for creative new adjacent research directions. This position is primarily a research position, though teaching experience can be obtained
-
Eastern Scheldt (Netherlands), and Cork Harbour (Ireland). THE DEPARTMENT The Department of Estuarine and Delta Systems (EDS, NIOZ-Yerseke) seeks to understand how interactions between organisms
-
and SETS approaches. The successful candidate will be able to adjust to different and unexpected conditions, work across disciplinary boundaries, and find creative solutions to research problems
-
of reliable and robust deep learning methods with clinical impact. The project is highly interdisciplinary, and the successful candidate is expected to interact with clinical and industrial partners
-
, including urban metabolism and SETS approaches. The successful candidate will be able to adjust to different and unexpected conditions, work across disciplinary boundaries, and find creative solutions
-
to interactive uncertainty visualisations and dissemination of results tailored to stakeholder needs. Your key responsibilities include: designing and implementing process-informed machine learning and data
-
deep learning methods with clinical impact. The project is highly interdisciplinary, and the successful candidate is expected to interact with clinical and industrial partners. Where to apply Website