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colleagues on multi‑omics data integration and analysis. You will also work with AI experts to help implement predictive models that improve guide design and functional genomics workflows. You will join an
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 6 hours ago
. Description: Biospheric modeling --- This postdoctoral position focuses on advancing our understanding of Earth's biosphere and complex feedbacks to weather and climate through the innovative integration
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to reduce the amount of required training data while maintaining high predictive accuracy. Methods and Techniques : Density Functional Theory, Machine Learning for atomistic modeling Location : Institut Jean
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Details The aim of this project is to combine nanomechanical methods with modelling (i) to develop quantitative, predictive models for the behaviour of molecules in sliding contacts, and (ii) to understand
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challenges of learning from network traffic, (ii) train original AI models that are designed to operate precisely on such data, and (iii) demonstrate the viability in production of AI-driven solutions for, e.g
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, to define novel biomarkers, and to identify novel therapeutical targets. We have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models
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workflows, and data engineering for mobility platforms • AI/ML for transportation prediction, system optimization, and environmental/health impact modeling • Deployment of decision-support tools for public
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intelligent decision architectures, predictive analytics, and adaptive computational models that can operate in dynamic, uncertain, and high-stakes project environments. The appointee will conduct original
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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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-driven methods provide excellent performance under low or cyclo-stationary regimes but struggle with highly dynamic and rapidly varying conditions; conversely, model-based state observers ensure robustness