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Field
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of a mentor, you will implement two-stage meta-analytic models and weighted least squares correlation methods to estimate the surrogacy strength of CGM-derived metrics (e.g., Time in Range (TIR
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Responsibilities • Develop and extend Bayesian semi-mechanistic renewal equation models for estimating genotype-specific reproduction numbers and immune escape. • Build scalable inference pipelines integrating
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experience using Bayesian estimation methods (e.g., Stan, brms/rstan). Experience preparing DSMB reports or interim safety summaries for NIH-funded clinical trials. Experience curating and depositing research
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research will develop and apply novel Bayesian machine learning methods – in particular physics-informed Gaussian processes and/or neural operators– to build accurate probability density functions (PDFs
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, active learning, Bayesian optimization, agentic AI, or closed-loop materials discovery. Experience in computational heterogeneous catalysis, electrocatalysis, surface science, electronic-structure analysis
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and climate adaptation worldwide. Your research will develop and apply novel Bayesian machine learning methods – in particular physics-informed Gaussian processes and/or neural operators– to build
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have the opportunity to contribute to various cutting-edge research areas. Several exciting research topics are proposed (not limited): Computer Vision / Remote Sensing for Agriculture: Explore
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Approximation calculations, whose direct use in Bayesian parameter estimation is currently computationally prohibitive. By providing a fast and statistically controlled surrogate for these calculations
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, multi-robot systems, distributed autonomous systems, or a closely related area. Essential Application/interview Experience with Bayesian methods such as Gaussian processes, particle filters for estimation
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of avian biodiversity information worldwide. Jointly, these approaches can provide complementary geographic and temporal coverage, and integrating them can improve estimates of species occurrence, phenology