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The Department of Electrical Engineering and Electronics is offering an exciting opportunity to work with Sony Computer Entertainment Europe Ltd (UK) to develop next generation gaming environments
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multidisciplinary research in broad program themes of Engineering Sciences for Computing and Data Science, Intelligent Systems and Materials, Advanced Electronics and Electrical Engineering, Sustainable and Smart
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positions, undergraduate positions, visiting assistant professors, and visiting research associates. Department College of Engineering and Computer Science (CECS) - Electrical and Computer Engineering Work
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single-cell multi-omics data, or genetic variants and functional genomics data), and apply the computational methods to integrative genomics data to understand tumor microenvironment, brain science, and
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University, Ole Worms Alle 3, DK-8000 Aarhus C. The affiliation will be with the Department of Agroecology. More information can be obtained from Professor Tommy Dalgaard (tommy.dalgaard@agro.au.dk , phone +45
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Two year postdoc position at Aarhus University for single molecule FRET based investigations of l...
molecules. The project is based in the team of professor Gregers Rom Andersen at the department of Molecular Biology and Genetics (MBG). Funding for the project is secured by a major grant from the Novo
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College of Engineering and Computer Science: The UCF College of Engineering and Computer Science is among the nation's largest producers of engineering and technology workforce talent, and we
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-AI), and retrieval-augmented generation (RAG). Tina Hernandez-Boussard, MS, MPH, PhD (link is external) , is Professor of Medicine (Biomedical Informatics), of Biomedical Data Science, of Surgery and
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), in addition to supporting multiple projects and data analysis as assigned. JOB RESPONSIBILITIES Frequently meeting with legislators or staff about their policy goals, priorities and research needs
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functional data ”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case