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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 20 hours ago
thermal-fluid discoveries. Postdoctoral Fellow (”"postdoc"") is a professional apprenticeship designed to provide recent Ph.D. recipients with an opportunity to develop further the research skills acquired
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 20 hours ago
provide recent Ph.D. recipients with an opportunity to develop further the research skills acquired in their doctoral programs or to learn new research techniques, in preparation for an academic or research
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Description 50% Laboratory work: Prepare buffers/samples; develop experimental techniques; design and conduct experiments; analyze data; keep detailed notes and records pertaining to this laboratory work. 30
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(incubation chambers, growth chambers, greenhouse hydroponics, aeroponics)As part of these overarching job responsibilities, tasks may include: •Prepare, collect and organize samples for microbial, growth
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% protected time for scholarly activity/research, and 15% protected time for professional development (e.g., seminars, mentorship, case consultation, etc). The position is one year, with the possibility
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of scientific AI. Focus Areas: Cross-Domain Interoperability: Develop common readiness templates, standardized metadata models, and APIs to enable seamless integration across diverse scientific domains
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In 2014, our team developed a compact proton linear accelerator (linac) design for an accelerator-driven system (ADS) aimed at nuclear waste transmutation, based on state-of-the-art niobium
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. Final salary will be based on equity and available budget. Responsibilities 50% Research (e.g., study design and protocol development, participant recruitment, data analysis, manuscript writing, grant
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development CAROW advances the work of faculty and institutes conducting translational labor research and brings them together to drive new insights. Responsibilities: This position will contribute to ongoing
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on developing machine-learning surrogates for electronic structure and electrostatic potential and using these models to predict structural and electronic evolution under applied bias. Methods may include density