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experience in one or more of: large-scale data analysis, time-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department
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field (e.g., geography, resource management, environmental studies/science, or related disciplines) with strong experience in causal inference research. The ideal candidate will be a highly motivated
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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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, Micro-C/Hi-C, BS-Seq/EM-Seq), massively parallel enhancer assays (ATAC-STARR-seq), and comparative/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle
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of interacting particle methods for Bayesian inversion by including model error in the likelihood evaluation. As model problem, we will consider the inference of parameters in phenomenological models for cardiac
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Experience developing pipelines and code for gravitational-wave searches and/or parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing
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computational analyses, as well as statistical inference, for models describing the proliferation, mutation, and selection of blood cell precursors in human bone marrow. A primary focus will be advancing
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of the following topics will be appreciated, but mostly we look for smart people who enjoy learning new things: Approximate Bayesian inference Differential geometry Numerical computations (ideally with experience in
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through to large-scale individual-based simulation as well as statistics and Bayesian inference. This highly motivated, collaborative research group leads funded, international consortia in modelling, NTDs
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development and statistical modelling in resilience assessment (e.g., dynamic/latent-variable models, Bayesian hierarchical models, causal inference, time-series analysis, cognitive modelling) Build robust