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of the experimental approach will include: Bayesian reconstruction of events on billion-year timescales, determination of optimal embeddings and encodings for protein structures, multiple structural alignments
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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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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
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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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imaging data. Building on recent critiques of overfitted prediction models, the project will implement a structured, multi-tiered analytical framework, comparing classical regression techniques with modern
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consideration market benchmarks, if and when appropriate, and internal equity to ensure fair compensation relative to the university’s broader compensation structure. We are committed to offering competitive and
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suitable evolutionary models Development and implementation of novel phylogenetic approaches, including those implementing protein structural information. Where to apply Website https
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disease, systems biology (including the modeling of signaling pathways and biomolecular structure-function relationships), and stochastic modeling of disease processes such as progression, detection, and
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candidates with strong expertise in Bayesian methods, uncertainty quantification, and/or machine learning applied to nuclear theory. The group’s research spans a wide range of topics including nuclear
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forecasting. Familiarity with ensemble methods, Bayesian approaches, and uncertainty estimation. Experience with large-scale or messy real-world data (structured and/or unstructured). Interest in or experience