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21 Nov 2025 Job Information Organisation/Company University of Amsterdam (UvA) Research Field Computer science » Informatics Computer science » Programming Researcher Profile First Stage Researcher
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, their achievements and productivity to the success of the whole institution. The Faculty of Computer Science, Institute of Theoretical Computer Science, the newly established Chair of Algorithmic and Structural Graph
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About the Project The future power grid will be a highly complex cyber-physical system, integrating multiple distributed energy resources (DERs) such as solar, wind, marine, and bioenergy alongside
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the Informatics Institute, in particular, Intelligent Data Engineering (INDElab) group, and their activities; assist in educational activities and supervision of bachelor and master students; complete and defend a
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imaging using deep learning. You will study the water imbibition in hierarchically porous Si‑based material systems across multiple length and time scales. These systems can manipulate fluid transport
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for individuals living with multiple long-term conditions known as multimorbidity, and may lead to unnecessary polypharmacy. This PhD studentship aims to develop a Bayesian modelling framework to identify clusters
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-matter interactions, via theory and simulations, to measure quantum dynamics in atoms and molecules. The project aims to explore new ideas for how quantum tomography, combining multiple measurements and
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application! We are looking for a PhD student in AI and autonomous systems with a focus on Vision-Language-Action (VLA) Models to control multiple heterogenous robots Your work assignments The primary focus
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21 Nov 2025 Job Information Organisation/Company University of Amsterdam (UvA) Research Field Computer science » Informatics Computer science » Programming Researcher Profile First Stage Researcher
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works have shown that for embedding hierarchies, we should abandon Euclidean geometry altogether and operate in hyperbolic space [1]. Our lab has published multiple papers showing that hyperbolic deep