2,062 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"Cardiff-University" positions at University of Michigan
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populations. Strong organizational skills, attentive to detail and able to follow protocols Basic computer skills - word processing, internet searches, spread sheets, Microsoft Outlook, additional skills in
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equipment returned to Patient Equipment for cleanliness, safety and proper operation Return all equipment from patient floors and specified pick up areas to central location Instruct, upon request, medical
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-processing of moveable equipment/devices, including inlet gaskets. Set-up and testing of equipment such as ventilators, BiPAP, nitric oxide delivery machines and other medical devices. Responsible for learning
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working in a team-based environment Flexibility in schedule, including weekdays, evenings and weekends, averaging 20 hours per week Excellent oral communication skills Computer skills - word processing
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rising 6th through 12th grade participants. The curriculum consists of project-based learning that is split into courses that include English, engineering concepts, computer programming, and mathematics
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on building and maintaining professional relationships with diverse groups and individuals. Strong computer skills in Microsoft Office and Google suite of products. Modes of Work Positions that are eligible
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research projects in computer vision, machine learning, AI, and robotics. Projects may include physically-grounded AI guidance agents, modeling of multimodal data, and generative AI systems for situated
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for clinical use. Responsibilities include designing and implementing new methods in data processing and analysis, such as signal and image processing, machine learning, and advanced mathematical techniques (e.g
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threading machine or other mechanical method, and bend pipe by hand or with pipe-bending machine. Assemble and install pipe and pipefittings of brass, cast-iron, copper, lead, glass, plastic and other
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-processing of large, multimodal datasets. A major focus is the application and evaluation of machine learning and predictive modeling approaches to address research questions in partnership with faculty and