15 wireless-communication-engineering Postdoctoral research jobs at University of Virginia
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, Physics, Engineering, or a related field, earned within the past six years Experience with AI and machine learning Excellent written and spoken communication skills Demonstrated ability to produce high
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electrical engineering, biomedical engineering, computer science, medical physics, neuroscience, or a related field. While this is the preferred background, highly qualified candidates from other scientific
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across physics, mechanical engineering, materials sciences, network science, and more. This multidisciplinary and highly collaborative environment provides an exceptional opportunity for candidates
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science, engineering, sociology, economics, management, or related fields). We seek applicants with strong computational and statistical skills, experience with managing large datasets, and fluency with
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well as grow their scientific communication skills by writing scientific articles (research publications and reviews), presenting their work at UVA seminars and external conferences, and applying for funding
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Collaborate with leading researchers within and outside UVA Qualifications: U.S. citizenship required Ph.D. in Data Science, Statistics, Computer Science, Network Science, Physics, Engineering, Sociology
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. Minimum Qualifications: Education: Ph.D. or equivalent in engineering, physics, chemistry, or any biomedical science Preferred Experience: A strong background in the theoretical and experimental aspects
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well as testing effectiveness of implementation supports (e.g., professional learning communities and coaching). Responsibilities: Coordinating data collection and analysis activities. Developing and disseminating
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disparities for communities and populations. Candidates will receive mentorship and training in precision health, including advanced statistical methods focused on predictive modeling in relation to response
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their communications Data Collection and Analysis: Collect data on the implementation process, including facilitator performance and participant feedback Analyze fidelity data to identify areas where