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possible thereafter. The aim of this project is to advance the development of multi-trait Bayesian linear regression models that enable the sharing of genomic information across traits and biological layers
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learning, Large Language Models, Stochastic optimization, Transfer & Evolutionary optimization, Bayesian optimization for complex design in material and engineering. Key Responsibilities: Collect relevant
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, Bayesian modeling, and/or statistical machine learning. The Successful Candidate Will Ability to cooperate with other researchers and be an effective team player. Excellent written and oral
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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informatics approaches (e.g., machine learning, Bayesian statistics) and spatial data processing and analysis skills would be of advantage. Expertise in Stata, R, or other analytic tools. Strong communication
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(e.g., machine learning, Bayesian statistics) and spatial data processing and analysis skills would be of advantage. Expertise in Stata, R, or other analytic tools. Strong communication (oral and written
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candidate, you will: Develop and apply Bayesian Network machine learning methods to analyze the dynamics of G-protein coupled receptors to uncover allosteric regulation that enables design of allosteric
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topics ranging across programming language (especially Bayesian statistical probabilistic programming), statistical machine learning, generative AI, and AI Safety. Key Responsibilities: Manage own academic
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-dimensional data, survival and event history analysis, model selection and criticism, graphical modelling, non-parametric methods, machine learning, hierarchical Bayesian modelling, and time- and space
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, non-parametric methods, machine learning, hierarchical Bayesian modelling, and time- and space-modelling. The group emphasizes general methodological development, often motivated by real-world