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Read Inti's storyClose this story Does this sound like you? You have a PhD in a relevant field (e.g. cognitive or affective neuroscience, computational psychiatry; human decision sciences). You have a
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
machine learning. The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity
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are expected to have a PhD in Physics, Mathematics, Computer Science or a related quantitative discipline. Ideal candidates from the biological sciences should have experience with scientific programming. Ideal
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Job Description Collaborates with new and existing program partners to support research initiatives, including the collection, analysis, and interpretation of primary and secondary data to address
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-erklæringen var sist oppdatert 23.02.2026 Hva er en cookie? En cookie er en liten datafil som lagres på datamaskinen, nettbrettet eller mobiltelefonen din. En cookie er ikke et program som kan inneholde
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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complexes Small gas-phase molecules and organic chiral compounds Compute accurate transition moments beyond the electric-dipole approximation and simulate x-ray observables, including x-ray absorption, x-ray
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive models for polymer-based materials. This project aims to leverage
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, providing direct feedback on theory analysis and further predictions. The goal is to develop a theoretical and computational approach that has strong predictive power for finding completely new types