Sort by
Refine Your Search
-
Listed
-
Employer
- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
- Leiden University
- University of Twente (UT)
- Maastricht University (UM)
- European Space Agency
- Radboud University Medical Center (Radboudumc)
- University of Amsterdam (UvA)
- Wageningen University & Research
- Universiteit Leiden
- University Medical Center Utrecht (UMC Utrecht)
- Utrecht University
- 2 more »
- « less
-
Field
-
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
-
Project description This position is part of a bilateral research consortium that works on the integration of different spectroscopic techniques connected to fermentors to realize real-time monitoring, with
-
tasking in real time. The fellowship will investigate how mission planning can evolve from static scheduling to an adaptive, intelligence-driven process executed directly on board satellites. This includes
-
to addressing the major societal challenges of the future. The research carried out at the Faculty of Science is very diverse, ranging from mathematics, information science, astronomy, physics, chemistry and bio
-
. Garmin watches), smartphones (e.g. EMA, mPath), passive sensing and small cognitive tasks. Challenges that will be targeted in this project are how to do (real-time) integration of subjective and objective
-
discipline; their aim is to benefit science, and to contribute to addressing the major societal challenges of the future. The research carried out at the Faculty of Science is very diverse, ranging from
-
experimental colleagues. A desire to develop the direction of your project is very welcome! Job requirements Masters in Physics, Computer Science, Nanobiology, Mathematics, Bioengineering or a related discipline
-
develop interactive systems and interfaces that capture subjective experiences and deliver timely, context-aware feedback before, during and after training, and evaluate these systems in real-world studies
-
, polyvocal conversational agent. The agent will use real-time visitor feedback, including eye gaze, speech, and dialogue content, to infer interactional states such as engagement, curiosity, openness
-
, subjective user feedback, and environmental data. The research will involve machine learning, human-centred experimentation, real-time comfort prediction, and the integration of intelligent climate control