23 computational-physics-"https:"-"https:"-"https:"-"https:"-"https:" research jobs at Virginia Tech
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Job Description We are seeking a highly motivated Postdoctoral Associate - Community-Centered Physical AI for Smart Urban Management to contribute to a 1-year research agenda on community-centered
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collaborators. Required Qualifications • PhD in neuroscience, physics, computer science, applied mathematics, biomedical engineering, or a related quantitative field. PhD awarded no more than four years prior
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Sciences, Geodesy, Physics, Mathematics, Computer Sciences, Civil & Environmental Engineering, or a related field. PhD awarded no more than four years prior to the effective date of appointment with a
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of agentic AI systems for accelerating computational catalysis and experimental design. The successful candidate will contribute to building AI-native frameworks that combine physics-based modeling, machine
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and security challenges, build sustainable solutions, and provide expert perspective on using AI. Required Qualifications - PhD in computer science, statistics, data analytics, business administration
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and collaborative researcher with expertise in engineering, materials science, or related computational fields to contribute to our research program focused on transport packaging optimization and
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characterization. The successful candidate will contribute to an externally funded research program focused on understanding structure-performance relationships in molybdenum-based zeolite catalysts for methane
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Job Description A postdoctoral associate is sought to conduct research in the area of integrated circuits at Virginia Tech's Institute for Advanced Computing with an appointment in the Bradley
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optimize LIFU as a new human brain mapping tool and for translation to clinical therapies in addiction, mental health and pain. Knowledge in basic computer skills, record keeping and experience with human
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that can be repurposed for viruses with pandemic potential. This includes working closely with computer scientists to utilize published “omics” datasets and machine learning approaches to identify FDA