Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
- European Space Agency
- Amsterdam University of Applied Sciences (AUAS/HvA))
- Leiden University
- Royal Netherlands Academy of Arts and Sciences (KNAW)
- Tilburg University
- University of Amsterdam (UvA)
- University of Twente (UT)
- Wageningen University & Research
- Erasmus University Rotterdam
- University of Groningen
- University of Twente
- Vrije Universiteit Amsterdam (VU)
- 4 more »
- « less
-
Field
-
funded through 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 Leverage computer vision, smart
-
We are a leading international university where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact — today and in the future. TU/e is home...
-
Shape the future of AI and Computer Vision at TU Delft. Lead research. Inspire engineers. Create impact. Job description You will shape the future of computer vision and AI by developing
-
, enabling trustworthy computation without compromising energy budgets. Treating security as a first-class citizen when designing hardware components and future computer architectures requires novel design
-
control and grid-forming/grid-following operation; HVDC, MTDC and offshore systems; protection; machine and load modelling; model order reduction; parameter estimation and model validation; large-scale
-
parameters are candidate biomarkers for tumor angiogenesis, with prostate cancer as the initial clinical focus. This PhD position is part of MOMENTUM, a project funded by the NWO Open Technology Programme that
-
, uncertainty-aware parameter estimation, surrogate modeling, and reduced-order modeling where relevant. The scientific emphasis is mechanics-first: we are looking for someone with strong foundations in nonlinear
-
to identifying the dominant physical processes and determining how system behaviour changes under varying forcing conditions and parameter regimes. Model results will be evaluated against observations from several
-
resource acquisition trait parameter space, and perform scenario analyses tailored to a set of Swiss lakes. There will be close collaboration with the other PhD candidate and Postdocs of the projects
-
Neural Nets (PiNNs) or “ML inspired” traffic models. PhD2 focuses on data assimilation and estimating start and boundary conditions such as path-flows, and other key parameters and inputs. In your role as