Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Groningen
- Radboud University
- University of Twente
- Wageningen University and Research Center
- Leiden University
- Eindhoven University of Technology (TU/e)
- Utrecht University
- Delft University of Technology (TU Delft); Delft
- University of Twente (UT)
- Eindhoven University of Technology (TU/e); Eindhoven
- Leiden University; Leiden
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); Amsterdam
- University of Twente (UT); Enschede
- CWI
- Delft University of Technology (TU Delft)
- Erasmus University Rotterdam
- Maastricht University (UM)
- Maastricht University (UM); Maastricht
- University of Groningen; Groningen
- Wageningen University & Research
- Wetsus - European centre of excellence for sustainable water technology
- Radboud University; Nijmegen
- Vrije Universiteit Amsterdam (VU)
- KNAW
- Vrije Universiteit Amsterdam (VU); Amsterdam
- Wageningen University & Research; Wageningen
- Amsterdam UMC
- Amsterdam UMC; Amsterdam
- Delft University of Technology
- Erasmus MC (University Medical Center Rotterdam)
- Erasmus MC (University Medical Center Rotterdam); Rotterdam
- Erasmus University Rotterdam (EUR)
- Erasmus University Rotterdam (EUR); Rotterdam
- Radboud Universiteit
- Radboud University Medical Center (Radboudumc); Nijmegen
- Radix Trading LLC
- Royal Netherlands Academy of Arts and Sciences (KNAW)
- Tilburg University
- Tilburg University; Tilburg
- University Medical Center Utrecht (UMC Utrecht)
- University Medical Center Utrecht (UMC Utrecht); Utrecht
- Utrecht University; Utrecht
- VU Amsterdam
- 34 more »
- « less
-
Field
-
, practitioners and researchers have realized that predictions made by machine learning models should be transparent and intelligible. Although explainable AI methods can shed some light on the inner workings
-
Join us to explore the mechanics of soft matter through a unique blend of theory, hands-on experiments, and machine learning. Job description Soft matter such as polymers and hydrogels
-
identification and machine learning. The key challenge is striking a balance between, on the one hand, modelling the physical, dynamic and nonlinear behavior of the components with sufficient physical accuracy
-
Computational Fluid Dynamics (CFD) models; data-based models determined from training/calibration data by system/parameter identification and machine learning. The key challenge is striking a balance between, on
-
Are you interested in writing to learn and learning to write in the age of AI? As a PhD candidate, you will investigate these topics at the intersection of (foreign) language learning and
-
interested in writing to learn and learning to write in the age of AI? As a PhD candidate, you will investigate these topics at the intersection of (foreign) language learning and technological developments
-
computational model to capture the complex transport of gases, liquids, and charges in these porous structures, including the complex interfaces between them. Insights from the model will directly guide the
-
theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be opportunities to present at leading
-
geospatial workflows on an abstract level, using purpose-driven concepts and conceptual transformations; develop AI and machine learning based technology to automate the description and modeling of data
-
researchers in soft robotics, control theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be