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probabilistic forward model (a digital twin) that maps microstructure to electrochemical performance. This involves simulation-based inference and physics-informed machine learning techniques that can quantify
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: Bayesian hierarchical deconvolution of spatial bins using matched snRNA-seq reference, cell-cell communication inference, and spatial niche identification Multi-omics integration: linking spatial and single
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equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
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conduct research in several areas: analysis of high-dimensional data, Bayesian methods, spatial-temporal models, non-Gaussian modeling, applied research in social science, as well as stochastic models and
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specimens to estimate historical age structures over the last 150 years. Forecasting Shifts in the Pollination Service Window. The researcher will use Bayesian inference (e.g., Integrated Nested Laplace
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/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle delivery. The postdoc will join Professor Nathaniel R. Street’s team at UPSC, working closely with
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, etc.) development of predictive models and digital decision-support tools for nutrition and health method development in causal inference, integration of heterogeneous data sources, uncertainty