Sort by
Refine Your Search
-
Listed
-
Employer
- Delft University of Technology (TU Delft)
- University of Groningen
- Radboud University
- University of Twente
- Utrecht University
- Wageningen University & Research
- Delft University of Technology (TU Delft); today published
- Delft University of Technology (TU Delft); yesterday published
- Eindhoven University of Technology (TU/e)
- KNAW
- Leiden University
- Leiden University; Leiden
- University of Twente (UT)
- University of Twente (UT); Enschede
- Wageningen University and Research Center
- Amsterdam UMC
- Delft University of Technology
- Delft University of Technology (TU Delft); Delft
- Delft University of Technology (TU Delft); 17 Oct ’25 published
- Delft University of Technology (TU Delft); 3 Oct ’25 published
- Eindhoven University of Technology (TU/e); today published
- Elestor BV
- Maastricht University (UM)
- Maastricht University (UM); Maastricht
- Maastricht University (UM); today published
- Maastricht University (UM); yesterday published
- Tilburg University
- University of Groningen; Groningen
- Wetsus - European centre of excellence for sustainable water technology
- 19 more »
- « less
-
Field
-
handling, enabling first-time-right manufacturing. The predictive quality of these tools relies on accurate constitutive models that describe the behavior of the molten material during forming. With
-
the second direction, you will explore the geometric design of nonlinear systems. Using nonlinear reduced order modelling (ROM) integrated with optimization algorithms, you will design structures
-
ecology; experience with programming and simulation is an advantage. Excellent analytical and critical thinking skills, with the ability to relate model structure to outcomes and interpret them in
-
the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you eager to help advance and upscale
-
reducing demand. However, demand drivers are manifold, including technology advancements, population and economic trends, and their future developments come with deep uncertainties. Infrastructure policies
-
: Multiscale modelling to better understand RFB behavior and identify optimal hierarchical shaped pore- and electrode-structure to encounter optimum electrolyte as well as electrical flow. Prototyping
-
dynamics and learning in artificial and biological neural networks, with the aim of: Unveiling the link between network structure and neural representations. Understanding the impact of structural and
-
can lead to defects during hot press forming. Accurate prediction of such defects requires both careful experimental characterization of frictional behavior and advanced constitutive models to describe
-
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
-
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