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well as predictive models based on machine-learning technologies, in order to carry out code development and testing activities within the listed projects; therefore, skills in software design and development
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Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP) | Paris La Defense, le de France | France | about 1 month ago
a postdoctoral researcher to work full-time on the DiscoReel project. The postdoc will work on developing machine and deep learning methods for epidemic modeling, integrating them with mechanistic
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successful candidates will dedicate their efforts to the following specific research objectives: (1) Developing models for predicting the thermal runaway (TR), venting, and jet fire in a single cell with
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system. Climate models are important tools for improving our understanding and prediction of atmosphere, ocean, and climate behavior. We seek candidates with an interest in advancement of radiative
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repurposed into remediation tools. Experimental data will be integrated into a simplified first order soil–vegetation–atmosphere (SVAT) model, a “box” model, enabling the prediction of radionuclide
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for transportation prediction, system optimization, and environmental/health impact modeling Deployment of decision-support tools for public-sector clients (municipalities, MPOs, DOTs) Urban mobility, equity
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will generate synthetic data from personal data to mitigate scarcity in rare diseases. This will facilitate data availability for further development of predictive models, thereby enhancing the quality
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and refine the RG-based model to enhance its biological interpretability and robustness across different tumor types; to extend the model to simulate and predict solid tumor response to innovative
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, and process modelling. The position is available immediately, with a start date as soon as possible. Where to apply Website https://www.academictransfer.com/en/jobs/357300/postdoc-position-in-microbial
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models trained on the Year 1 dataset to predict promising compositions. The third year focuses on application and physical metallurgy insights, where the student will apply the refined methodology to a