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belowground; - development of SDM modeling structures that best reflect plant use of multiple resources (e.g., interactive vs. substitutable resources); - testing the application of resource colimitation theory
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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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colloidal routes, enabling precise control over size, morphology, composition, and structural complexity. This role offers an unparalleled opportunity to lead the computational core of a cutting-edge
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methodological theme involves understanding the geometric and topological structure of knowledge: how concepts cluster, how new ideas deform or extend existing structures, and how the shape of a knowledge
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. The candidate shall take part in the research group on “Statistical models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models
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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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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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single cells make decisions during differentiation, in particular during development. Building on Bonsai, a Bayesian framework that leverages tree structures for distortion-free exploratory analysis
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model classifiers (PLS-DA, random forest, neural network, etc) towards unraveling materials structure-function relationships, and are familiar with optimization approaches such as genetic search, Bayesian
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to study chemical transformations in materials. 2. Artificial Intelligence Applications: - Leveraging conventional machine learning techniques for materials property prediction and Bayesian approaches