617 phd-studenship-in-computer-vision-and-machine-learning PhD positions in Netherlands
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for candidates for a PhD in Computational Imaging for Cultural Heritage (1.0 fte) As a PhD student, you will be embedded in the research group Computational Imaging and Deep Learning (CIDL), part of the Leiden
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for Cultural Heritage (1.0 fte) As a PhD student, you will be embedded in the research group Computational Imaging and Deep Learning (CIDL), part of the Leiden Institute of Advanced Computer Science (LIACS). As
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Apply now The Faculty of Science and Leiden Institute of Advanced Computer Science (LIACS) are looking for candidates for a PhD in Computational Imaging for Cultural Heritage (1.0 fte) As a PhD
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groundbreaking opportunities in nanomedicine, adaptive materials, and self-healing systems. This PhD project, part of the NWO-XL collaborative initiative "Joining Forces for Human-Made Molecular Machines," focuses
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to design and control synthetic molecular machines offers groundbreaking opportunities in nanomedicine, adaptive materials, and self-healing systems. This PhD project, part of the NWO-XL collaborative
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PhD Towards Human-Made Molecular Machines: Functional DNA-Rotator Motors (V25.0077) « Back to the overview Job description Molecular machines are essential for biological processes such as molecular
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PhD position Modeling modals: A computational study of modal verb acquisition (V25.0196) « Back to the overview Job description The Center for Language and Cognition Groningen is seeking a motivated
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Organisation Job description The Center for Language and Cognition Groningen is seeking a motivated PhD candidate in the fields of linguistics, computational linguistics, language acquisition, and
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-Performance Computing. This is a unique opportunity to contribute to the foundations for tomorrow’s machine learning. Your job The FoRECAST project aims to develop theory (e.g., categorical semantics and
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, Computer Science, Mathematics or a related field; demonstrable programming skills, preferably in Python; solid experience with machine learning, in particular with graphical models such as Bayesian networks