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Field
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expertise in machine learning, computational imaging, computer vision, or signal processing. Proficiency in scientific programming and modern ML frameworks, with the ability to implement and debug research
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for self-driving and/or human-in-the-loop experiments; (3) computer vision for extracting complex patterns, structure, and meaning from images and/or volumes; and (4) new mathematics and algorithms leading
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for high-dimensional dependent data, and data sketching approaches for massive data. Opportunities to Contribute: Develop statistical/machine learning methodology for multi-modal imaging data integration
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national Scientific Society meetings. Minimum Education and/or Work Experience M.D. or PhD Required Qualifications M.D. or PhD Required Certificates/Credentials/Licenses N/A Computer Skills General office
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis
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, STATA, SAS, or other statistical software; Exposure to pre and postprocessing of magnetic resonance imaging and/or magnetic resonance spectroscopy data or willingness to learn neuroimaging techniques
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are invigorated by the creative potential of our global collections to impact teaching, learning, and research across Cornell University, and to broaden our reach as a world-class academic art museum. The Johnson
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include: Biomedical sensing and physiological monitoring Edge intelligence and energy-efficient machine learning hardware Radar and wireless signal processing and communications The successful candidate
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, scientific, and regulatory steps required to translate in vitro molecular hits into small animal preclinical evaluation models. Processing and integrating complex transcriptomic, proteomic, and image-based
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biofluid samples Conceive, prototype, and benchmark new computational and AI methods for liquid biopsy, including techniques to bridge to other data modalities, e.g. imaging Apply modern machine learning