30 genetic-algorithm-computer-"UCL" Postdoctoral positions at Virginia Tech in United States
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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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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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. - 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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Job Description Dr. Shenglin Mei lab at the Fralin Biomedical Research Institute (FBRI) Cancer Research Center- DC is seeking highly motivated Computational Biology postdoctoral associate to join
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supervision of Prof. Yingda Cheng on computational methods and modeling for kinetic equations. The research conducted will involve development of numerical methods, development and analysis of reduced order
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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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, 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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Job Description The Department of Computer Science, the Department of Electrical and Computing Engineering, and the Innovation Campus at Virginia Tech will be jointly hosting a Postdoctoral
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include in vitro neural differentiation, gene expression manipulation, metabolic assays, and mouse breeding and behavior. Knowledge in basic computer skills, record keeping and experience with data
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microenvironment. The project will employ genetically engineered mouse and human glioma models, along with advanced imaging techniques, single-cell and spatial transcriptomics, and molecular biology approaches