33 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Linköpings-University" Fellowship positions at University of Michigan
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automation, computational drug design, machine learning, and software engineering. The ideal candidate will contribute to innovative research and the development of advanced computational tools within our lab
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, machine learning and AI-based computational tools to advance biomedical as well as oncology research. The fellow will work under the supervision of Dr. Veera Baladandayuthapani and there will be
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. Experience working with medical images. Very good computer programming skills and physics background is essential. Desired Qualifications* Nuclear medicine imaging/dosimetry experience. Experience in image
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discipline or a related field - Strong demonstrated verbal and written communication skills Desired Qualifications* - Familiarity with manufacturing systems and processes - Experience with machine learning and
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of design and fabrication of medical devices, 3D printing, microfluidics, mechanics of materials, biomaterials, blood-material interactions, computer aided design, and/or in vitro/in vivo evaluation
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medical, dental and vision coverage effective on your very first day 2:1 Match on retirement savings Responsibilities* Researching and developing novel machine learning architectures for integration across
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skills. Strong organizational and multi-tasking skills. Good documentation and communication skills. Ability and willingness to learn and implement novel experimental techniques. At least 1 year of wet
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. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes . Additional Information
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reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes . Additional Information The position provides a
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are at the discretion of the hiring department. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn