22 music-programming-computer Postdoctoral positions at Virginia Tech in United States
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MATLAB or similar programming languages). A basic knowledges and deep interest in neuroscience are required. Preference will be given to candidates with experience in in vivo neurophysiology and/or
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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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and optimization of measurement-based quantum computing protocols for quantum simulation of quantum many-body models. Preference will be given to candidates familiar with the stabilizer formalism and
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candidate will play a key role in developing and advancing new models and simulations for Computational Fluid Dynamics (CFD) hypersonic codes. Specific tasks include developing new turbulence and transition
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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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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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, particularly turbulence in the boundary layer; developing new high-speed measurement techniques, particularly using optical diagnostics methods; augmenting experimental data with Computational Fluid Dynamics
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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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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