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science, computational sciences, or mathematics is preferred. Knowledge required includes machine learning and statistical methods, proficiency in programming with C, C++, and Python, familiarity with Cloud systems and
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at Knoxville. The work will involve modeling, focusing on network and game theoretic models. An ideal candidate would have some experience in computational simulation, mathematical models of infectious disease
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, population ecology, spatial ecology, mathematical biology, or other similar fields. Demonstrated experience building and analyzing mathematical and statistical models aimed at answering biological, ecological
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Basic Qualifications: A Ph.D. in computer science, applied mathematics, plant biology, or related fields. Applicants cannot have received the most recent degree more than five years prior to the date
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engineering, applied mathematics, physics, or related field with an evidence of understanding nuclear nonproliferation and/or the nuclear fuel cycle. Experience applying machine learning techniques to problems
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of Tennessee, Knoxville, TN, United States Applications are invited for a postdoctoral research associate position in the Department of Physics & Astronomy at The University of Tennessee, Knoxville to begin in
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Memphis, TN in the lab of Dr. Amber Smith. Postdocs will develop and analyze mathematical models of the immune response to influenza virus infections and coinfections. The Smith lab is a dual dry/wet
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University of Tennessee Health Science Center | Memphis, Tennessee | United States | about 2 months ago
Systems at the University of Tennessee Health Science Center (UTHSC) in Memphis, TN in the lab of Dr. Amber Smith. Postdocs will develop and analyze mathematical models of the immune response to influenza
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Systems at the University of Tennessee Health Science Center (UTHSC) in Memphis, TN in the lab of Dr. Amber Smith. Postdocs will develop and analyze mathematical models of the immune response to influenza
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matter experts, and geographers to deliver prototypes. Basic Requirements: Requires a Ph.D. in electrical and computer engineering, computer science, applied mathematics or related area, completed within