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targets to treat anhedonia. Proposals that challenge prevailing assumptions, employ cutting-edge technologies, or integrate machine learning with neurobiological data are especially welcomed. Projects
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approaches for using machine learning to analyze X-ray data, particularly Resonant Inelastic X-ray Scattering (RIXS). The position will collaborate with experts in RIXS experiments (Mark Dean), computational
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of faculty supervisor, develop novel techniques incorporating machine learning in particle physics event generators. Contribute to the development of machine learning driven techniques in the Pythia 8 event
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for biomedical/healthcare and green energy applications, big data and AI in smart sensor technology, and quantitative and systems biology. These efforts are supported by infrastructural and internationalisation
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the health and MedTech sectors, and beyond. For more information about the total announced post-doctoral positions within in the AMBER co-fund project please visit https://www.euraxess.se/jobs/392999
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beyond ITER with advances in HTS materials, manufacturing, sensors and machine learning. High current density cables are required for the design of compact HTS magnets in a fusion pilot plant (FPP
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community! The position This position will develop and maintain data-processing pipelines for large-scale neuronal and behavioral datasets, including multi-day high-frequency recordings, fluorescence
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. In addition, you must have: a solid foundation in energy technology and a strong understanding of artificial intelligence (AI), machine learning (ML), and data-driven modeling documented experience
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sociology. Strong quantitative skills and experience with large-scale data analysis required. Computer Science/HCI: PhD in Computer Science, Human-Computer Interaction, Information Science, or related
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/or spatial genomics, computational biology, machine learning, bioinformatics, and systems neuroscience. Prior experience with deep learning applied to biological data is a plus. Practical experience