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Current UC employees must apply internally via SuccessFactors Founded in 1819, the University of Cincinnati ranks among the nation’s best urban public research universities. Home to 53,235 students, more than 11,000 faculty and staff and 350,000+ living alumni, UC combines a Top 35 public...
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application consists of: An application Transcript(s) – For this opportunity, an unofficial transcript or copy of the student academic records printed by the applicant or by academic advisors from internal
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on cutting-edge research at the intersection of statistical modeling, artificial intelligence, and infectious disease epidemiology. This position focuses on developing and applying statistical, mathematical
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Stig Brøndbo 4th August 2025 Languages English Norsk Bokmål English English Faculty of Engineering Science and Technology PhD Fellow in signal processing and modelling in the seafood industry Apply
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modelling and analyses as agreed with Scottish Government, the formulation and submission of research publications and research proposals, as well as help manage and direct this complex and challenging
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(NorESM). In the project, the CLM model will be using the FATES (Functionally-Assembled Terrestrial Ecosystem Simulator) terrestrial ecosystem model and the MIMICS+ module for soil carbon decomposition
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reinforcement learning, computational rationality and user modelling, and simulator-based inference. The post-holder will work on a project from Professor Kaski’s UKRI Turing AI World-Leading Fellowship. Research
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of epidemiological modelling and analyses as agreed with Scottish Government, the formulation and submission of research publications and research proposals, as well as help manage and direct this complex and
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Modelling to join an interdisciplinary team investigating how ecological traits, environmental changes, and anthropogenic pressures have shaped historical declines and extinctions of Australian mammals
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Do you want to further your career by applying your quantitative data analysis and modelling skills as part of an international multidisciplinary project on developing early warning systems