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Smart Stormwater Systems journey. You will help deliver a real-world demonstration site that turns award-recognised research into practice. The site will show how distributed storages, smart design and
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, control, and planning algorithms under environmental constraints. - Implementation of distributed simulation architectures (client-server, remote computing). - Analysis of simulation data and comparison
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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healthcare application needs to analyze sensitive patient data across distributed nodes. Researchers and students can explore privacy-preserving algorithms and technologies like federated learning and zero
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group of experts to predict (probabilistically) whether these occupations will be automated, augmented or unaffected by emerging technologies. Using this data, a classification algorithm is then trained
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different projects with different research objectives, depending on candidate interests and skills including some or all of the following activities. Consumer energy resources and distribution system
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at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need
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estimation methods for deep neural networks. A principled Bayesian framework for multimodal uncertainty modeling. Robust learning algorithms under missing modalities and distribution shifts. New uncertainty
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energy resources. The expected outcomes include technical advancement of distributed algorithms for managing energy resources at customer premises. The benefits include more resilient, secure, private, and
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Education Bayesian Uncertainty Estimation for Robust Single- and Multi-View Learning in CV and NLP Robust Active Learning Under Distribution Drift Data-Efficient Deep Learning for De Novo Molecular Design