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inform more targeted prevention strategies. Key objectives include: Analyzing Inequality and Prevention: Conduct empirical research to assess how prevention policies and healthcare innovations affect
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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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theories from tractable models (probabilistic circuits) and Bayesian statistics to tackle the reliability of machine learning models, touching topics such as uncertainty quantification in large-scale models
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planning tool which models how autonomous agents make decisions in an uncertain world. This project focuses on designing systems of interaction between agents in order to achieve complex, multi-objective
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manufacturing are critical for their commercial viability and long-term performance. The objective of this proposal is to utilize synchrotron techniques to explore why these solar cells, especially those based
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model system to validate the findings from the bacterial assays and provide a proof of concept for NP-induced mutational and epigenetic effects in higher organisms. A translational objective is to compare
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: detection of objects and relations between objects, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions carried out and
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version control and containerization (Docker/Singularity) Statistical Modeling: Quantitative data analysis using GLMs, Bayesian methods, or mixed-effect models to interpret complex perturbation datasets
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diagnostics, as well as general computer vision applications including autonomously guided vehicles (particularly self-driving cars), image-based localization, structure-from-motion, and object recognition
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about the diverse physical and geometric properties of objects and dynamic changes in the environment. This involves leveraging rich sensory data—such as vision and touch—encoding knowledge from