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and with the 2AT team at Institut Pprime to develop an innovative jet-noise prediction tool. The researcher will develop a novel jet-noise prediction tool based on a resolvent analysis of the Navier
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refines algorithms and workflows for crop and pasture monitoring, modeling, prediction, and decision support and automation; Supervises graduate research assistants and student interns working
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predictive models, and interpreting large environmental datasets, collaborating in interdisciplinary projects and in the production of scientific publications. In the performance of duties, it may sometimes be
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SyMulDaM project involving the development of predictive models to quantify the integrity and durability of a nuclear power plant containment structure., within the mechanical engineering department
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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the conditions of crop, pasture, and their environment with advanced remote sensing and geospatial technologies; Develops and refines algorithms and workflows for crop and pasture monitoring, modeling, prediction
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predictive models for evaluation of the role of dietary in health and disease and establish personalized dietary strategies for more effective disease prevention. In many cases, the work involves time series
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/trait data) Conducting statistical modeling, feature selection, and predictive analytics for forest health, resilience, and biomass estimation Supporting data preprocessing, cleaning, normalization, and
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of complex epidemiological and genetic data, in computational and population health sciences and in disease risk-modelling and risk-prediction. Eligibility criteria The project will suit students with strong
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currently lack reliable uncertainty estimates, limiting error detection and automation. The UMLFF project aims to develop next-generation MLFFs with built-in uncertainty predictions to enable safe, automated