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Position Summary: A one-year post-doc in Mathematics at Yeshiva College and Stern College for Women open to all areas, possibly renewable for a second year (the renewal is subject to budget approval
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time-dependent transport processes in the kidney; develop a computational framework that is applicable to different animal models; run simulations, provide mathematical analysis and critical evaluation
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, collaborative science, and high-quality research. Minimum Education and Experience Ph.D in a related field. Preferred Education and Experience Doctoral degree in mathematics, statistics, computer science
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roadblocks and develop a testing schedule that provides data early enough to make meaningful changes What We Think Will Be A Good Fit BSc, MSc or PhD in HCI, Mathematics, Statistics, Psychology, Anthropology
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Details Posted: 03-Apr-24 Location: Irvine, California Type: Full-time Salary: Open Categories: Academic/Faculty Physical Sciences Internal Number: 5150741 University of California Irvine Associate Researcher -- Rignot Group The Rignot research group in the Department of Earth System Science,...
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mathematics, statistics, applied physics, etc. Candidates with a professional doctoral degree (MD, PharmD, DNP, DDS, DO, etc.) are also welcome to apply if they have a demonstrated track record in the relevant
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(applied mathematics, physics, geoscience or engineering). Record of scientific publication in international journals, research performance, and professional achievement commensurate with experience. Proven
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candidate) in a quantitative discipline, including Computational Biology, Bioinformatics, Computer Science, Bioengineering, Biophysics, Biostatistics, Mathematics, Statistics, Electrical Engineering
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. Developing software in Python and other modern programming languages Mathematical programming, statistical modeling, or machine learning Recognizing and properly handling confidential and sensitive information
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. Developing software in Python and other modern programming languages Mathematical programming, statistical modeling, or machine learning Recognizing and properly handling confidential and sensitive information