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development. Experience with implementing statistical learning or machine learning (e.g. Bayesian inference, deep-learning). Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro
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shown that DLVM models can be extended with success to at least two different types of data (network and texts, text and images, …) but the extension to several data types is still difficult in the sound
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the different crystals might have different orientations. As metals are typically used for load-bearing applications, it is imperative to understand the mechanical performance of such materials. Initially, metals
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, kernel machines, decision trees and forests, neural networks, boosting and model aggregation, Bayesian inference and model selection, and variational inference. Practical and theoretical understanding
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, please see Degree equivalency Experience Candidates should have experience in the following areas: Experience with Bayesian modelling and inference. Experience characterising machine learning models in
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Bayesian approach (Lages, 2024). Techniques used: Computational modelling, Bayesian inference, sampling and simulation techniques, prior distributions and posterior predictive checks, model comparison
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Sequential Monte Carlo Methods for Bayesian Inference in Complex Systems School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Lyudmila Mihaylova Application Deadline
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Aim/outline Graphs or networks are effective tools to representing a variety of data in different domains. In the biological domain, chemical compounds can be represented as networks, with atoms as
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coronavirus), and the production of renewable energy in different countries are some examples. In almost all contexts, these episodes happen in several time series, but not necessarily at the same calendar