32 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Virginia Tech
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jurisdictions utilizing land use-value assessment estimates. Duties include, but are not limited to: development of computational methods, maintenance of current models and data sets, identifying and testing
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Alexandria, Virginia. The focus of these positions will be on quantum computing, quantum algorithms, quantum learning, quantum error correction, and quantum fault-tolerance. The successful candidate will join
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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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models and machine learning techniques for kinetic equations arising from plasma and neutron transport. The position will be based at Virginia Tech’s campus in Blacksburg, VA. The postdoc will have a
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-based models, and remote sensing technologies. Required Qualifications • Ph.D. in Civil or Environmental Engineering, Hydrology, Data Science, Geosciences, Computer Science, or a related field. PhD must
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. - Strong proficiency in machine learning, optimization algorithms, and computational modeling applied to construction systems. - Experience with designing and conducting experimental studies to evaluate
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. Experience with both experimental and computational approaches is desirable, as is proficiency in widely-used microbiological methods. The Aylward lab is a vibrant and dynamic work environment that welcomes
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, materials, virology, computational biology, science communication, community engagement, and ethics. Candidates should demonstrate all relevant qualifications (see required and preferred qualifications below
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datasets, machine learning, and experimental methods to investigate how the tumor microenvironment and gene regulatory factors control tumor metastasis cascade. By advancing our understanding of malignant
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critical role in advancing computational materials science by developing and applying first-principles and machine learning methods, with a focus on interatomic potential development and large-scale