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. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in R and/or
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Bayesian machine learning to improve risk management for bridge portfolios. We offer a funded PhD position in an excellent research environment. The project Our infrastructure is aging, and decisions about
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. Desirable Familiarity with supply chain management, operations, or organizational contexts. Experience with advanced statistical methods (e.g. multilevel modelling, causal inference, Bayesian methods
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work closely with the other PhD candidate of PAST, who creates high-resolution proxy-based reconstructions of the same paleoclimate. Together, you apply a Bayesian statistical framework to contrast and
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cells Key methods will include: Gaussian Processes (heteroscedastic & multivariate) Operator-valued and deep kernels Active Bayesian experimental design Physics-informed neural networks Closed-loop
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receive training and skills in some of the following: meta-barcoding, stable isotope analysis, trophic-web analysis, Bayesian statistics, wet-lab experimentation – respirometry, fieldwork. Previous
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, stable isotope analysis, trophic-web analysis, Bayesian statistics, wet-lab experimentation – respirometry, fieldwork. Previous experience in any of these areas is useful but not essential. Diving
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for experiments using reinforcement learning, Bayesian methods, image analysis and data analysis. Collaborate with interdisciplinary teams, including machine learning experts, device modelling specialist
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analysis, Bayesian Skyline Plots, PCA, Bayescan - information provided in the CV and/or in the motivation letter; Other professional experience: teaching activities in evolutionary biology and phylogenetics
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 2 months ago
modeling, or machine learning - Experience with large-scale genomic data analysis (e.g., GWAS, QTL, PRS, or multi-omics integration) Strong programming skills in R or Python; familiarity with Bayesian