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catalysis. Experience in materials synthesis and experimental measurements. Training and experience in kinetic modeling. Expertise in in-situ spectroscopy and scientific literature review. Fluency in spoken
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of morphing drone prototypes by combining structural optimization, lightweight design, and experimental testing. The research assistant will: Participate in CAD/CAE modeling and structural optimization of UAV
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materials must include: • Letter of Interest – Describe your relevant training and experience. Please include details about specific research skills you have developed, community-based settings you have done
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Multiphysics, FLAC / FLAC3D, Abaqus, ANSYS, MATLAB, or comparable tools, including experience implementing user subroutines or custom extensions for advanced material modeling or customized workflows. • Ability
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Appointment Start Date: Immediately Group or Departmental Website: https://med.stanford.edu/lulab.html (link is external) How to Submit Application Materials: Email the required application materials to bingwei
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, engineering, computational science, a physical science (materials science, chemistry, physics etc.), or related field. Hands-on experience with AI frameworks and employing large language models. Strong Python
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background in nonlinear optics, ultrafast photonics, and integrated photonics, alongside the ability to develop predictive models for optical materials and photonic devices. The successful candidate will work
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and lead and support advanced research on West Nile virus (WNV) dynamics, with a primary focus on computational infectious disease modeling and quantitative analyses of field data. They will contribute
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/evidence, reproducible AI-in-the-loop pipelines, or evaluation/verification of model-generated outputs) is preferred. The initial appointment is funded for one-year from the date of hire with a possibility
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processes and particle acceleration in geospace. Research activities will integrate observational data analysis, theoretical modeling, and numerical simulations to advance our understanding of magnetospheric