29 phd-scholarship-in-computational-material-science Postdoctoral positions at Virginia Tech
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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
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Job Description Dr. Zhiwu (Drew) Wang’s research team at Virginia Tech invites applications from highly motivated candidates specializing in environmental engineering, specifically focused on PFAS
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for writing academic publications, including grant reports and publications. Required Qualifications • PhD in Mathematics, Engineering, or a related field with a background in statistical and mathematical
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on their research and innovation campus. Required Qualifications - PhD and/or MD in Computational Biology, Bioinformatics, Genomics, Biology, Data Science, Computer science or other related fields. PhD must be
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development of 5G testbed being developed at Virginia Tech and be involved in UAV experimentation with 5G. Required Qualifications - PhD in Computer Engineering or Computer Science or a related field. PhD must
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Appalachian Blue Ridge Mountains. The Tholl lab welcomes applicants from diverse backgrounds. Required Qualifications - PhD degree in Molecular Biology, Biochemistry, Plant Science, or related field
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training opportunities at Virginia Tech, including preparation for going on the job market. Required Qualifications The postdoc’s PhD must be conferred prior to the date of appointment and conferred within
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geophysical and geochemical observations. Required Qualifications • A PhD in geophysics, planetary science, or related field of study by appointment start date. PhD awarded no more than four years prior
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The School of Animal Sciences (SAS) at Virginia Tech is searching for a post-doctoral associate to develop a novel research line and carry out extension efforts that are focused on human- and animal
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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