Sort by
Refine Your Search
-
Listed
-
Employer
- Delft University of Technology (TU Delft)
- Utrecht University
- European Space Agency
- University of Twente
- Leiden University
- Maastricht University (UM)
- University of Twente (UT)
- ARCNL
- Eindhoven University of Technology (TU/e)
- Radboud University Medical Center (Radboudumc)
- Universiteit Leiden
- University of Amsterdam (UvA)
- University of Groningen
- University of Utrecht
- 4 more »
- « less
-
Field
-
and voltage collapse, converter-driven instability, interoperability under different control strategies, transition from GFM to GFL and vice versa, black start capability. Work out how such models
-
with a track-record in actuator design and soft robotics, and skills in modelling, you will pioneer new concepts in chemically-driven robotics. In this 2-year project, you will develop innervated soft
-
system models that reproduce the phenomena UTOPYS is meant to study at this part of the power system, such as islanding, frequency and voltage collapse, converter-driven instability, interoperability under
-
capabilities interact with operational conditions, human intervention and the wider maritime and port logistics systems. The postdoctoral researcher will develop data-driven and model-based approaches to support
-
, integrating data-driven approaches such as inverse reinforcement learning and imitation learning with cognitive and socio-technical models. The research will also contribute to the development of a broader
-
combining geophysical sensing, physical modelling, and artificial intelligence. You will: · design and conduct research using geophysical techniques such as Ground Penetrating Radar (GPR), Electrical
-
at least one of the following areas: crop modelling, plant sensing or data-driven crop management. Demonstrable experience in translating research insights into practical applications and collaborating with
-
forming fundamental challenges. Physics-based models are too complex, while pure data driven models lack explainability, ultimately reducing the robustness. The proposed solution is the development of a
-
or neural networks) on CFD data to develop a fast, data-driven wind field predictor. You will combine this surrogate model with AeoLiS and evaluate the accuracy of the new model setup by applying it to
-
data to learn contextual representations of microbes and communities and translate them into predictive models for successful crop microbiome engineering. Your job Plant-associated microbiomes can