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24 Jul 2026 Job Information Organisation/Company THE UNIVERSITY OF HONG KONG Research Field Biological sciences Computer science Mathematics Researcher Profile Recognised Researcher (R2) Established
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, predictive modeling, machine learning, and quantitative research to trusted enterprise data. Working within the UNG NEST, the position develops analytical models and predictive insights that improve strategic
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generating functions, and discovered algebraic P-fields, initiating an algebraic-geometric construction of A-model Landau–Ginzburg string theory. His recent interests include the mathematical formulation
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: Experience and/or expertise in early literacy, early mathematics, STEM education, child development, inclusive practices, curriculum development, family engagement, assessment, and educational technology
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Eligible for Overtime College/Division: College of Science and Mathematics Department: 100006 - Biology Department Pay Band: Pay Rate: Commensurate with Experience Specify Range or Amount: Is this a JMU only
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Foundation project titled: Mathematical theory of next generation fluid-structure interactions models, at the PMF’s Department of Mathematics – 1 person. Where to apply Website https://posao.pmf.hr/opening
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theory and non-standard logics, preferably within one of the two branches of the project: Modal Logic of Forcing Inner Models from Extended Logics These are active fields within set theory, with many
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designing and creating optimal processes and procedures to systematize the data reporting process. The incumbent will use advanced data modeling, predictive modeling, and analytical techniques to develop
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designing and creating optimal processes and procedures to systematize the data reporting process. The incumbent will use advanced data modeling, predictive modeling, and analytical techniques to develop
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Łukasiewicz Research Network – PORT Polish Center for Technology Development | Poland | about 2 months ago
. The candidate will join ongoing projects in our team and perform mathematical/computational modeling. The topics include (1) constructing deep learning-based models to predict the molecular microenvironments