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University of California San Francisco | San Francisco, California | United States | about 2 months ago
sources, including developing a Bayesian Hierarchical Modeling framework; (2) using integrative modeling approaches to characterize heterogeneous protein assemblies structures and dynamics; (3) developing
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Bayesian risk quantification for accelerated clinical development plans (C4-MPS-Oakley) School of Mathematical and Physical Sciences PhD Research Project Competition Funded Students Worldwide Prof J
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. This robust combination drives substantial advancements in optimization, sampling, inference, and machine learning. On one side, statistical approaches such as Bayesian inference play a critical role in
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software engineering, Bayesian modeling and approaches to data analysis. Key Responsibilities: Preprocessing and data scientific approaches to analyzing human behavioral data Computational model development
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Menopausal Women” with full-time employment for a duration of 3 years, starting in February 2026. Objective of the project: BrainAGE is a machine learning-based biomarker that estimates biological brain age
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culture of care and a healthier campus environment where everyone can thrive. Future and current OSU employees can use the Benefits Calculator to learn more about the full value of the benefits provided
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Benefits Calculator to learn more about the full value of the benefits provided at OSU. Key Responsibilities This position is responsible for the day-to-day coordination and implementation of quantitative
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-dimensional niche models, and applying advanced Bayesian spatio-temporal methods. You will: Build n-dimensional abiotic niches for >6,700 species and estimate population positions within them. Quantify niche
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annotation using bespoke references, and downstream perturbation-level and gene-level effect estimation, as well as the development of sophisticated approaches and biologically grounded perturbation prediction
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there are innumerable examples of its application, one important observation is the low proportion of studies proposing the estimation of uncertainties (<5%). Yet uncertainties can be multiple and of different natures