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on advancing Predictive, Preventive, Personalized, and Participatory (P4) approaches in health and medicine. Within the IRAP framework, the project’s scientific goal is to discover and validate novel therapeutic
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AI researchers from ANITI, IMT and CERFACS, as well as with researchers/engineers in weather forecastings from the CNRM (Météo-France). Hybridization methods between neural networks and physical models
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new vegetation model. The new EEO-based vegetation model should then also be used to predict future transitions and biome shifts to ultimately answer the question to what extent C4 grasslands
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for the next 2.5 years at the interlink of prevention and prediction of wildfire risk, by contributing to the development of a fundamental physical model to understand the process of fire spread for
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | about 1 month ago
learning models for antimicrobial activity prediction (e.g., Weka); - Strong communication skills; - Fluency in English (written and spoken). The candidate must demonstrate interest in microbiology and
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an import element in the prediction of reactor-scale operational scenarios providing compatibility to both, required heat and particle exhaust constraints and good fusion plasma core performance. Given
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models to study metabolic diseases is preferred. For a complete list of Publications please visit here: https://www.ncbi.nlm.nih.gov/myncbi/1J54E41I5YVku/bibliography/public/ Your qualifications should
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methods to integrate transcriptional and cellular dynamics. Analyze large-scale transcriptomic and spatial dynamics datasets. Work in close collaboration with the team's biologists to test predictions from
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work on research projects employing latent variable modeling and risk prediction methods to better understand substance use related morbidity and mortality outcomes (e.g., overdose, hospitalization
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review, Asana and JIRA for agile project management, and Qlik Cloud, Snowflake and Posit Connect for deploying dashboards and predictive models to support the University’s sophisticated fundraising