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developing a novel imaging and amperometry-based platform for research into neurological diseases. About us The Esbjörner lab belongs to the Division of Chemical Biology , which is part of the Department
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Associate will assist a Principal Investigator Leon Shterengas in the Department of Electrical and Computer Engineering . The incumbent will conduct research on semiconductor photonic device nanofabrication
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include technique development work aimed at combining imaging techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
: * Introductory proficiency in Python, R, or another programming language * Prior exposure to machine learning or AI techniques in clinical research * Experience using deep learning frameworks * Familiarity with
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on behalf of Stony Brook University. BSA salutes our veterans and active military members with careers that leverage the skills and unique experience they gained while serving our country, learn more at BNL
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Education and Experience Preferred experiences include research in cognitive neuroscience, human functional brain imaging, psychology, neurology, psychiatry, machine learning, data visualization, and data
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processes relevant to human health and disease using using genetics, molecular biology, biochemistry, cell biology, and microscopy/imaging. A qualified candidate will be capable of working independently
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molecular biology skills; proficiency with standard word, statistical, and imaging processing software (e.g., Microsoft Word, GraphPad Prism, Adobe Illustrator). Job Duties Job Duty Implement genetic, cell
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also include technique development work aimed at combining imaging techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a
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The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our