Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Delft University of Technology (TU Delft)
- University of Amsterdam (UvA)
- Utrecht University
- Eindhoven University of Technology (TU/e)
- University of Twente
- Erasmus University Rotterdam
- European Space Agency
- Leiden University
- AMOLF
- Amsterdam UMC
- Radboud University
- Vrije Universiteit Amsterdam (VU)
- Erasmus University Rotterdam (EUR)
- KNAW
- Maastricht University (UM)
- Radboud University Medical Center (Radboudumc)
- Tilburg University
- University of Twente (UT)
- Wageningen University & Research
- 9 more »
- « less
-
Field
-
integrated and functional transmembrane organic ion transporters. Develop a protein-based sensor for intracellular toxin detection. Integrate synthetic cells on dialysis membranes and set the first steps
-
processes are affected in the diseased brain by combining cutting-edge techniques such as in vivo and ex vivo electrophysiology recordings, live-imaging of calcium indicators and neurotransmitter sensors
-
interaction: from brain-computer interfaces to social robots and from sensor technologies to interactive sports and play systems. Many projects at the HMI-group delve into how such interactive technologies may
-
explore new ways to build sensors, such as superradiant clocks and collaborate with industry, startups and users to bring quantum technology to the market . This project is embedded in the Quantum Delta NL
-
particular, this allows for fully flexible local mesh refinement in the space-time cylinder. You will develop suitable aposteriori computable estimators for the discretization error to adaptively steer
-
, treating time as yet another dimension. In particular, this allows for fully flexible local mesh refinement in the space-time cylinder. You will develop suitable aposteriori computable estimators
-
individual building level, directly contributing to reducing network congestion in the Netherlands. Your work will have real-world impact: the models you develop will be tested and validated together
-
and individual building level, directly contributing to reducing network congestion in the Netherlands. Your work will have real-world impact: the models you develop will be tested and validated
-
continuously and collaboratively from wearable or mobile sensor data without compromising user privacy. Your efforts and collaborations with other European Union partners will contribute to the advancement
-
network (based on U-net, e.g. similar to MitoNet that we used before on FAST-EM data) to automatically segment organelles from FAST-EM imaging data, with a focus on WPBs, endolysosomal compartments and