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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
the University of North Carolina at Chapel Hill. These appointments will be in the Institute's Center for Environmental Modeling and Policy Development (IE-CEMPD). The selected candidates will work with Dr. Sarav
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/embeddings, probabilistic graphical models, or causal/relational inference. Background or strong interest in meta-research / scientometrics / scholarly communication, including citation-based analysis, claim
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vivo experimental models (e.g. ticks and mice) to identify and characterize bacterial virulence factors and host cell machinery; molecular, cellular, and immunological mechanisms that facilitate
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models (mathematical + software) of existing grid operational practices Systematically exploring different formulations of mixed-integer constraints in grid optimisation problems Developing machine
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when liaising with collaborative research partners. Your key responsibilities will be to: develop advanced modelling, novel material synthesis, processing, fabrication and manufacturing sequence
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and antibiotic alternatives Testing candidate interventions using in vitro and in vivo challenge models to assess pathogen colonization and host immune function The postdoctoral fellow will work closely
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library, mixed reality projects, and pre-clinical cardiac medical device testing. Duties/Responsibilities: Regularly assist with preclinical research studies, including large animal models and isolated
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materials. Skilled oral and writing communication skills with a good record of publication and external recognition in relevant areas. Ability to model Argonne’s Core Values: Impact, Safety, Respect
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funding. You will be responsible for the development of novel acquisition, reconstruction, image analysis and/or modelling methods for cerebrovascular magnetic resonance imaging (MRI) to improve
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Wide Field Science Teams. Duties will include contributing to RGES-PIT science by developing new galactic models/dust models and hierarchical occurrence rates analysis pipelines; preparing papers