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Field
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Qualifications • Highly recommended by scientists in the candidate’s field of study • Expertise in one of the following areas: Environmental or Performance Physiology, Machine Learning, Motion Analysis, Multiscale
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Strong quantitative analytical skills including but not limited to causal inference design, estimating and interpreting cross-sectional and panel models, and data visualization Ability to work with various
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modeling and techno-economic assessment as appropriate. The candidate will work independently under general guidance, collaborate effectively within a multidisciplinary team environment, and prepare
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Summary The Computing and Robotic Construction Laboratory at the Department of Civil, Environmental, and Geospatial Engineering, Michigan Technological University invites applications for a
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
Research Associate position involves laboratory research in biomedical science. The position studies focus on an animal model of arthritis and include in vivo studies (muscles and behavioral analyses
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Qualifications Candidates with experience in any of the following areas are especially encouraged to apply: - Engineering non-model microorganisms or environmental isolates - Multi-omics techniques (e.g
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. This position resides in the Multiscale Modeling and Materials by Design (M2MD) Group within the Materials Science and Technology Division (MSTD), Physical Sciences Directorate (PSD) at Oak Ridge National
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an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model. In its research approach, the UM6P promotes transdisciplinary
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, and agent-based modeling for care delivery Health equity, patient access, and system resilience Multi-modal data integration using EHR, claims, environmental, and behavioral datasets The successful
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synthesis, characterization, drug release studies, antibacterial assays, and biological evaluation under in vitro, ex vivo, and in vivo conditions using a porcine model. The project will also utilize