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approaches capable of guiding experiments, interpreting results in real time, generating predictive models of materials synthesis processes, and refining experimental strategies under a Human-In-The-Loop
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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 concepts
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Fellow in organoids and organs-on-chip W/M. Our project aims to develop a multi-organ-chip platform to predict COPD patient responses to biologics and choose the most effective biologics. Main activities
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Reactor) designs. This system constitutes a corium containment strategy in the event of a severe accident. Therefore, understanding the thermodynamic properties of the corium is essential for predicting its
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incorporate clinical, lifestyle, and nutritional factors to build predictive models through advanced bioinformatics and machine learning. By identifying molecular signatures that distinguish responders from non
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knowledge of statistical and computational research methods (e.g., predictive/user modeling), cognition/affect tracking, measurement theory, analyzing high-frequency time series data, experience with
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Objectives: - Completion of the characterization of nanomateriais on in vivo models of vaccination - Completion of the characterization of nanomateriais on in vitro and in vivo cancer models - Analysis
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model predictions with biological knowledge and external data sources. Work closely with academic partner groups and the Innovation & Business (I&B) team to align technical development with biological
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from semantic radio maps; learn which features act as reliable predictors of rewards or outcomes; associate these features with predictive models that guide decision-making; exploit such cue–outcome
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research, teaching, and promoting an environment that allows all members of the department to thrive. Principal concentrations include weather prediction, air quality, air-sea interactions, climate modeling