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/or semantic machine learning models and techniques, network and textual vector embedding, natural language processing, and/or advanced statistical methods for Bayesian or causal analysis Familiarity
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networks. You should have experience of building machine learning models for environmental applications. A high level of data science and computational expertise is essential, as is experience with Bayesian
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Description Distribution estimation algorithms for abductive inference (total or partial) in dynamic domains. Structural learning of dynamic Bayesian networks with discrete and continuous variables (parametric
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The role The Atmospheric Chemistry Research Group (ACRG) and School of Engineering Mathematics at the University of Bristol have developed GATES, a graph neural network (GNN) machine learning model
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analyses. Current Major Projects Rapid Research in Diagnostics Development for TB Network (R2D2 TB Network) - This NIH U01-funded project has established a platform for identifying promising early-stage
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description Third-cycle subject: Applied and computational mathematics The Department of Mathematics at KTH is announcing a PhD position in Mathematics with a specialization in AI, focusing on Bayesian inverse
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, Applied Machine Learning, Neural Networks and Deep Learning as well as Machine Learning for AI and Data Science and Bayesian Theory and Data Analysis. We are looking for an associate able collectively
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Influenza Research and Response (CEIRR) as part of the NIH NIAID CEIRR collaborative network (https://www.ceirr-network.org/centers/penn-ceirr ). The participant will have the opportunity to attend local and
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candidates with strong expertise in Bayesian methods, uncertainty quantification, and/or machine learning applied to nuclear theory. The group’s research spans a wide range of topics including nuclear
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localise greenhouse gas emissions over large open areas enabling organisations to achieve their net-zero climate goals. Our sensor products generate large volumes of data as they scan the environment