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Field
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main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
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in knowledge-informed machine learning. The ideal candidate will have a strong background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov
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) for engineering systems. Our research covers surrogate modeling, reliability analysis, sensitivity analysis, optimization under uncertainty, and Bayesian calibration. We are known for developing the UQLab software
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, natural language processing, transformer based language models, generative image models (e.g., GAN and variational auto encoders), generative models for structured data (e.g., Bayesian networks), Blockchain
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recovery trajectories and injury patterns. Integrate personalized physiological measurements into a recovery prediction model, while adapting Bayesian Neural Networks for SCI data and analyzing the impact on
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campaigns including programmed screening or Bayesian optimisation. You will characterise the resulting materials, in terms of their properties and performance for an intended application. Sustainability will
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., GAN and variational auto encoders), generative models for structured data (e.g., Bayesian networks), Blockchain-based decentralized trust computing, software engineering/model driven design and
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, Joshua W. and D.L. Dowe (2005). ``Minimum Message Length and Generalized Bayesian Nets with Asymmetric Languages'', Chapter 11 (pp265-294) in P. Gru:nwald, I. J. Myung and M. A. Pitt (eds.), Advances in
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University of Texas Health Science Center San Antonio | San Antonio, Texas | United States | 3 months ago
data analysis, secondary data analysis, meta-analysis, clinical trials, missing data, structural equation modeling, and Bayesian methods. The Department seeks to identify successful candidates with
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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