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. Computational and bioinformatic skills. Experience in microscopy. Generation and analysis of mouse models. Handling of human samples. Molecular biology skills including CRISPR, cloning and qPCR. In vitro cell
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
universities in the nation for federal research expenditures as well as for federally funded social and behavioral sciences research and development. Here at Carolina, our highly skilled postdocs play a vital
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.) level program in Biochemistry, Chemistry, or a related field. Candidates must have demonstrated expertise in at least two of the following areas: bioinorganic chemistry, structural biology, microbial
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). The training program is designed to train the next generation of scientists and build a workforce equipped with expertise in integrating advances in biomedical engineering, technology, and Artificial
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. Collaborative work and teaching within the research team (10%) Qualifications Required Qualifications: Education: - Doctoral degree in bioinformatics, biostatistics, computational (micro)biology, genomics
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Apply now Job no:535376 Work type:Faculty - Pro Tempore Location:Eugene, OR Categories:Instruction, Computer and Information Science, Data Science Department: CAS CIS Computer & Information Science
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to further develop the initiative. Developing the Scholar’s own research program Please note this is a two-year appointment with an expected start date of July 1, 2025. Compensation, Benefits, and Perks
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various lab materials to ensure efficient functioning of the lab. Qualifications Essential Qualifications: PhD in biology, genetics, cell/molecular biology, biochemistry, or related fields is required. Pay
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incident angles for benchmarking and validation of theoretical calculations and computational physics and chemistry modeling of important surface processes occurring at plasma-material interfaces in fusion
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models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science