34 phd-position-for-research-in-computer-vision Postdoctoral positions at Virginia Tech
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and collaborative researcher with expertise in engineering, materials science, or related computational fields to contribute to our research program focused on transport packaging optimization and
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to incorporate these methods into models based on systematic testing and validation relative to current and historical data. This position will work closely with Virginia’s Land Use-Value Assessment Program
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Job Description Applications are invited for a National Science Foundation funded (LEAP HI Program #2051685), Postdoctoral Associate position with the System Performance Laboratory (SPL
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motivated and collaborative researcher with expertise in engineering, materials science, or related computational fields to contribute to our growing research program in sustainable food packaging. Current
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CRISPRai and optogenetic control systems and developing predictive metabolic models for the oleaginous yeast Yarrowia lipolytica. This position offers a unique opportunity to conduct cutting-edge research
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the possibility of extension to a third year. The position involves research in collaboration with one or more members of the tenure-line Virginia Tech Math faculty and teaching one course per semester. The primary
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of this work, this position is not eligible for telework. Required Qualifications • Ph.D. in Sustainable biomaterials, wood science, engineering or related field. PhD must be awarded no more than four years
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research, develop extension materials, and contribute to peer-reviewed publications. The position offers opportunities for professional development in applied research, science communication, and stakeholder
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of Engineering as well as the new Virginia Tech Institute for Advanced Computing (IAC) located in the Greater Washington, D.C. area. The position involves conducting research in signal processing, machine learning
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. The position involves research in modeling and simulation of within-host virus dynamics, theoretical immunology and multiscale immune-epidemiological models of infectious diseases and teaching two classes per