325 computational-material-science-"Multiple"-"Prof"-"Humboldt-Stiftung-Foundation" Postdoctoral positions
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Field
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understanding of biochemistry and materials chemistry with the ability to perform assay development. The successful candidate must be able to develop independent protocols for complex novel assays for multiple
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Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
to be involved in experiments in the Sheikh lab. Minimum Education and Experience Requirements PhD in Computational Biology, Bioinformatics, or a related field. Required Qualifications, Competencies, and
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) Doctoral Degree (or foreign equivalent) in Computer Sciences, Engineering, Neuroscience, Psychology or a closely
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Position Details Position Information Recruitment/Posting Title Postdoctoral Associate, Multiple Vacancies Job Category Staff & Executive - Research (Laboratory/Non-Laboratory) Department RWJ
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embryos This Human Frontier Science Program (HFSP) (link is external) funded project is in collaboration with the labs of Hervé Turlier (CIRB-CNRS) and Chema Martin (Queen Mary University of London). We
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project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research
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Experience Ph.D. in Chemistry or a related field and knowledge of quantum chemistry and quantum physics to develop two-dimensional superconducting single-photon detectors. Other Qualifications To the extent
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energy-related research challenges in the Greater Permian Basin region. This position will support the State of Texas Advanced Resource Recovery (STARR) program at the Bureau of Economic Geology (BEG
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project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research