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email ([email protected] ), an updated Curricular Summary in the FAPESP model , a motivation letter explaining their suitability and alignment with the project “Forensic discrimination of Brazilian
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electrocatalysis: The research involves the application and development of ab initio methods, molecular dynamics and kinetic Monte Carlo simulations to model electrocatalytic oxidation and reduction reactions
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learning, understand their mathematical foundations, and connect them to space-related technologies and missions. The focus is on building rigorous models that explain and predict the behaviour of modern
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science, machine learning or a related field. Strong experience working with clinical, biomedical, genetic, electronic health record, or health administrative data. Experience with large language models
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statistical models (Bayesian approaches, geographical analyses) adapted to environmental data. • Carry out statistical analyses and the spatial distribution of risk between residential environmental exposures
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. ● Experience with mouse models. ● Experience with cell and molecular biology techniques including PCR, western blotting, flow cytometry, cell culture and immunohistochemistry assays. Position Summary
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Qualifications: Ph.D. and/or M.D. in an area relevant to cell biology and/or immunology, with a strong publication record. Experience with in vivo models of cancer and/or infectious disease strongly preferred
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communication Ability to work effectively as part of an interdisciplinary team Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Additional Information While these are not
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on the development of continuum and discrete, stochastic mechanical models of ordered cellular structures and understanding the role of order in pattern formation. The project is in close collaboration with
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, mechanical and interfacial behaviour, loading and release, and device-to-device variability. You will then establish repeated sampling and delivery protocols in mammalian cells and corneal endothelial models