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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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Categories:Research / Scientific Job Description Dr. Shenglin Mei lab at the Fralin Biomedical Research Institute (FBRI) Cancer Research Center- DC is seeking highly motivated Computational Biology postdoctoral
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leveraging advanced computational methods to address critical questions in pandemic prediction and prevention. Key Responsibilities: - Develop and apply modern machine learning algorithms and computational
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. Techniques we use include in vitro neural differentiation, gene expression manipulation, metabolic assays, and mouse breeding and behavior. Knowledge in basic computer skills, record keeping and experience
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Virginia Tech, Mathematics Position ID: VirginiaTech -PA [#26659] Position Title: Position Type: Postdoctoral Position Location: Blacksburg, Virginia 24061, United States of America [map ] Subject Area: Mathematics Appl Deadline: 2025/06/30 11:59PM (posted 2025/06/12, listed until...
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Job Description The Ye Lab is seeking a highly motivated Postdoctoral Associate to join our team and contribute to an NIH-funded project focused on understanding how glioma hijacks the tumor microenvironment to drive malignant progression. Our research lies at the intersection of glial biology...
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and the Bradley Department of Electrical and Computer Engineering at Virginia Tech, Blacksburg, VA. The position involves conducting experimental research in the broad scope of wireless communications
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interdisciplinary team at the NSF COMPASS Center, which integrates tissue engineering, stem cells, materials, virology, computational biology, machine learning, molecular environmental engineering, science
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, Software Defined Radio (SDR), Software Defined Network (SDN), Network Function Virtualization and Edge-computing systems design. Researchers will carry out research in network systems, with focus on large
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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