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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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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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The successful candidate will join a global network of Simons Postdoctoral Fellows in Strong Gravity and Black Holes that are part of the Simons-BHSG. This new, multidisciplinary and multinational collaboration
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climate data, simple physics-based models, and AI to deliver more accurate projections of how our climate will warm and recover in a net-zero future. As part of this project, you will contribute to develop
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The successful candidate will join a global network of Simons Postdoctoral Fellows in Strong Gravity and Black Holes that are part of the Simons-BHSG. This new, multidisciplinary and multinational collaboration
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project developing Bayesian causal inference methods for mediation analysis using Electronic Health Records (EHR) data. The Research Fellow will design and implement Bayesian methods and software
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employment. Position description The successful candidate will work within the research project “Advances in generalized Bayesian inference via differential-geometric methods” funded by the Research Council
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. This is a unique opportunity to contribute to the global transition to net zero energy by developing and applying cutting-edge tools for autonomous materials discovery. You’ll work within the Australian
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computing (HPC) environments and include data assimilation techniques in a Bayesian framework. Under the guidance of a mentor, the participant will identify and integrate multiple data streams into the model
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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