15 high-performance-computing-postdoc PhD scholarships at Leiden University in Netherlands
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Apply now The Faculty of Science and the Leiden Institute of Advanced Computer Science (LIACS) are looking for a: PhD Candidate, Secure Computation Technologies and Applications to Machine Learning
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inequalities in everyday life (houses, objects, mobility, and diet), and in performed settings (burials, feasts, and figurative objects), to reconstruct how social inequalities featured in these social arenas
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diet), and in performed settings (burials, feasts, and figurative objects), to reconstruct how social inequalities featured in these social arenas. The aim is to both quantify evidence for social
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inequalities appear more consolidated, for example in graves. This project will investigate social inequalities in everyday life (houses, objects, mobility, and diet), and in performed settings (burials, feasts
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diet), and in performed settings (burials, feasts, and figurative objects), to reconstruct how social inequalities featured in these social arenas. The aim is to both quantify evidence for social
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knowledge, collaborate across disciplines, and experiment in a friendly environment fully embodying academic freedom across all career levels. The core focus of CML is to perform research and education in
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theoretical problems. Good computational/programming skills in Matlab or Python High proficiency in written and spoken English Research at our Faculty The Faculty of Science is a world-class faculty where staff
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will: Systematically retrieve publicly available video data and additional sources to compile a high-quality dataset; Perform ethnomethodological conversation analysis and multimodal analysis
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potentially field) experiments to assess the role of soil biodiversity in enhancing tree performance under different scenarios of water availability. Collaborate with project partners to integrate experimental
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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 Deep learning for Electron Microscopy pipelines (1.0 fte) As a PhD