17 distributed-algorithm-"Prof" PhD scholarships at Utrecht University in Netherlands
Sort by
Refine Your Search
-
, which options become visible, and how agency is distributed between humans and algorithmic systems. In this PhD project, you will study AI-supported decision-making from a representational perspective
-
the money! REconstructing Belief systems behind Urban Intensification and Land-rent Distribution (REBUILD)”, funded by the Swiss National Science Foundation. Focusing on case studies in the Netherlands and
-
now As a successful candidate, you will be part of the international research project “Follow the money! REconstructing Belief systems behind Urban Intensification and Land-rent Distribution (REBUILD
-
theory-experiment collaboration in which a PhD candidate in Condensed-Matter Theory based at Utrecht University, supervised by Prof. Rembert Duine, collaborates closely with a PhD candidate in Experimental
-
Application deadline: 26 April 2026 Apply now Strengthen the Adapt! project with digital-historical research into the big history of crisis! As a PhD candidate, you will work under the supervision of Prof
-
accelerates the transition to animal-free biomedical innovations by promoting the development, validation, and implementation of new methods. More information For more information, please contact Prof. dr
-
learning algorithms. Personalizing user interactions by building models that adapt explanations to specific knowledge levels and interests of users, so that user modelling and formal reasoning transform
-
and it will be supervised by Prof. Dr. Niki Frantzeskaki , Assistant Prof. Dr. Katharina Hölscher and Assistant Prof. Martijn Kuller . Additional Information Benefits We offer: temporary position (0.8
-
in exciting scientific communities on innovation and technology assessment. The project will be supervised by Dr Matthijs Janssen and Prof Wouter Boon . Where to apply Website https
-
reinforcement learning Enhancing transparency and contestability of decision-making processes, taking a multimodal approach to reveal the reasoning behind complex AI-driven planning and learning algorithms