35 computational-modelling-optical-properties Postdoctoral positions at Virginia Tech
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. The candidate will apply a suite of statistical and physical models for integrating observations of different accuracies, improving predictions of future hazards. The work will further include research, writing
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test protocol development. • Experience with wood material properties, orthotropic material modeling, or joint behavior under cyclic loading. Pay Band {lPayScaleID} Overtime Status Exempt: Not eligible
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Carilion (VTC). About the Lab: The lab integrates state-of-the-art neural recording technologies, complex cognitive tasks, and computational models to investigate the neural basis of flexible cognition. From
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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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Job Description Prof. Debswapna Bhattacharya’s research group in the Department of Computer Science at Virginia Tech (https://people.cs.vt.edu/dbhattacharya/) seeks to recruit multiple postdoctoral
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minimum of one year eligibility remaining. • Strong background in computational modeling, AI-assisted modeling, microbial testing, or paper chemistry and structural evaluation. • Experience with fiber-based
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through peer-reviewed publications and/or preprints • Ability to work independently as well as collaboratively Preferred Qualifications • Experience using Drosophila (fruit flies) as a research model
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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
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, improving faculty pre- and post-award service, strengthening compliance support, generating better research intelligence, and creating a scalable model for responsible AI use in research administration
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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