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prior to the effective date of appointment with a minimum of one year eligibility remaining. • Strong background in computational mechanics, structural dynamics, or wood mechanics. • Experience with
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. - Strong expertise in multiscale/multiphysics modeling relevant to catalysis, and experience with machine learning models. - Deep understanding of reaction kinetics, thermodynamics, and structure-reactivity
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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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, fermionic and bosonic quantum codes, machine learning approaches to decoding, magic and nonstabilizerness in quantum codes, tensor-network methods for code construction and decoding, and resource estimation
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ownership: Work on funded projects with room to develop independent ideas and lead publications • Career focused mentorship: Structured mentorship tailored to your goals (academic faculty, industry scientist
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the whole scientist. The successful candidate will lead this project while growing toward research independence, supported by an individualized development plan, structured mentorship, and clear milestones
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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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Job Description The successful candidate will join a vibrant research environment in the group of Prof. Vito Scarola at Virginia Tech (scarola.phys.vt.edu). The research involves the construction