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Research Associate to contribute to a project focused on robust Bayesian inference with possibility theory. Robust inference is crucial for many real applications in which datasets are invariably corrupted
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and synthesis of novel materials. Key Responsibilities: Develop and apply generative AI models for materials discovery, leveraging deep learning, Bayesian optimization, and active learning. Integrate
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processing would be an advantage. Proficiency in statistical software (e.g., R, Python, SAS, or Stata). Experience with clinical informatics approaches (e.g., cluster analysis, machine learning, Bayesian
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informatics approaches (e.g., machine learning, Bayesian statistics) and spatial data processing and analysis skills would be of advantage. Expertise in Stata, R, or other analytic tools. Strong communication
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of analyzing large-scale population data. Experiences working with electronic health records (desirable). Understanding of clinical informatics approaches (e.g., machine learning, Bayesian statistics) and
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topics ranging across programming language (especially Bayesian statistical probabilistic programming), statistical machine learning, generative AI, and AI Safety. Key Responsibilities: Manage own academic
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machine learning, Large Language Models, Stochastic optimization, Transfer & Evolutionary optimization, Bayesian optimization or Complex Design Optimization. Key Responsibilities: Support and conduct