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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning
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physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or equivalent) in
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laboratory focuses on developing physics-based computational methods to study protein structure, dynamics, and drug design. For more information about Dr. Vaidehi’s lab, please visit here. As a successful
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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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-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
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | 19 days ago
, and systems engineering. The advancement and application of techniques such as Systems-Theoretic Process Analysis (STPA), structured expert elicitation, and Bayesian Networks (BNs) are foreseen, and
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appropriate conditions, it provides a confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods
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predictions. To mitigate these effects, advanced ML techniques such as Bayesian deep learning, probabilistic models, and uncertainty quantification methods can be applied to enhance model robustness