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We invite applications for a Postdoctoral Research Associate in developing AI tools to integrate multimodal health data such as genetics, electronic health records, and molecular measurements in
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science for quantum information hardware with the industrially mature solid state platforms of silicon/silicon germanium and silicon carbide spin qubits. The position will focus on heterogeneous integration
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to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services
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Requisition Details GENERAL REQUISITION INFORMATION EEO Statement: UNMC is an Equal Employment Opportunity Employer, including an equal opportunity employer of protected veterans and individuals
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) outlining a research project addressing the history of Danish botany and the Flora Danica volumes in the period 1840–1900 within the statement of future research plans and information about research
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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molecular biology analyses. May assist and supervise graduate and undergraduate students. 25% Cloning molecular biology 25% Plant bioassay 20% Record and analyze data/results 10% report research through
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across connected forest–lake ecosystems. By integrating multi-taxa field data, trait-based ecology, experiments, and advanced statistical analyses, TRACE aims to uncover how ecological processes propagate
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plans; access to U of A recreation and cultural activities; and more! The University of Arizona has been recognized for our innovative work-life programs. For more information about working at
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
on “Integrating AI into Aquatic Ecosystem Models to Decode Ecological Complexity” funded by Villum Fonden. Within that project, the focus is on exploring novel ways to infer information from environmental data