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Associate selected for this position will work as a member of an interdisciplinary team on research involving the use of statistical and machine learning methods for the development of Immune Digital Twins
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machine learning tools. The postdoctoral fellow will contribute to various aspects of the project, such as: * developing new theoretical and numerical approaches for determining the thermodynamic and
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opportunities for methodological and/or software development, as well as the integration of machine learning into the project, depending on the candidate’s interests. This position is one of three Postdoctoral
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healthy and tumor-bearing animals using machine learning and AI approaches; and (3) integration of PBPK and QSAR models with AI methods to develop AI-assisted computational approaches to support decision
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for this position will work as a member of an interdisciplinary team led by Dr. Colin Xu on Department of Defense (DoD)-funded research project involving the use of statistical and machine learning methods
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, LHC , Machine Learning , Neutrino , Neutrino physics and Astrophysics , Phenomenology , Quantum Field Theory , Theoretical Particle Physics , theory , Lattice QCD Appl Deadline: 2025/12/01 11:59PM
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Children's Cancer Center. The Postdoctoral Associate will finish experiments, data analysis, and manuscripts related to the IL18 mediated enhancement of CAR NKT anti-tumor activity project. The ideal candidate
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 4 hours ago
, repeatable coverage of fire-prone regions. When combined with modern statistical and machine-learning approaches, these data enable robust mapping of fuels, assessment of burn severity, estimation of biomass
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 4 hours ago
on the application of artificial intelligence and machine learning to solving complex trajectory design problems. Specific applications will focus on tour design strategies and trajectory design within other multi
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19.07.2022, Wissenschaftliches Personal The Machine Learning and Information Processing group at TUM works in the intersection of machine learning and signal/information processing with a current