Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Max Planck Institute (MPI) for Psycholinguistics
- University of Twente
- University of Twente (UT)
- Delft University of Technology (TU Delft); Delft
- European Space Agency
- Utrecht University
- CWI
- The Netherlands Cancer Institute
- The Netherlands Cancer Institute; Amsterdam
- Tilburg University; Tilburg
- Wageningen University & Research
- Wageningen University and Research Center
- 2 more »
- « less
-
Field
-
wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
-
wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
-
can focus on learning for planning, risk-aware motion planning under uncertainty, learning of interaction models, multi-robot learning, multi-modal prediction models, or other related topics
-
-informed machine learning) and integrating uncertainty quantification into these workflows. You are familiar with environmental or soil science applications (e.g., carbon, nitrogen, biomass modelling). You
-
-based knowledge with machine learning. You will work closely with the Utrecht University team and OpenGeoHub together with other project partners, to develop and implement surrogate and hybrid modelling
-
the outcomes of SCC surgery. Job Responsibilities: As a PhD candidate, you'll focus on: Develop cutting-edge AI models: Train state-of-the-art deep learning models to segment SCC and healthy tissues using both
-
delineation and improve the outcomes of SCC surgery. Job Responsibilities: As a PhD candidate, you'll focus on: Develop cutting-edge AI models: Train state-of-the-art deep learning models to segment SCC and
-
partners to reduce CO2 emissions in steel production using machine learning. You can find more information here . You will work on a theoretical and an applied project on data-enhanced physical reduced order
-
. The employment of machine learning techniques for guidance, navigation and control functions for increased autonomy on board with respect to environmental or modelling disturbances or mission-critical phases (also
-
records from satellite data, and/or improved methods of uncertainty characterisation, including the use of artificial intelligence and machine learning to improve or analyse satellite climate data records