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possession or distribution of material, reagents, or records belonging to their laboratory and any proposed disclosure of findings or techniques privately or in publications. Collegial conduct towards co
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analysis using various characterization techniques, including computed tomography, to create process-driven, microstructurally informed models. These models will be derived from the combination of process
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, with the possibility of renewal Appointment Start Date: ASAP Group or Departmental Website: https://med.stanford.edu/ir.html (link is external) How to Submit Application Materials: Please email all
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the development and testing of new materials. The work will involve reactor design and setup with gas flow capability and process optimization. Qualified candidates should have a Ph.D. in chemistry, physics
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to achieve this is through thermal actuation, using a laser to locally heat the multilayer, where the applied heat creates a shrinkage (or expansion) of the heated materials. The challenge is to create such a
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experiment at the EIC. The program includes data analysis involving polarized targets at Jefferson Lab as well as full detector and physics simulations for ePIC. In addition, the candidate will collaborate
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workshops. Qualifications (requirements for the position): Holds a PhD in nuclear physics, nuclear engineering or similar. Candidates with a PhD degree in computer science/engineering with experience within
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extraordinary access to computing resources, trans-disciplinary international researchers across many disciplines within catalysis, materials and computer sciences, robotics, power electronics, and other academic
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have experience in mathematical modelling or simulations, preferably of biological systems. We encourage candidates from diverse departments, such as physics, computer science, applied mathematics
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of computational materials discovery starting in Summer 2025 . Our group is seeking to discover novel materials for renewable energy applications using high-throughput quantum chemistry calculations and data-driven