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Field
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approaches. Machine Learning in Geotechnical Engineering: Utilising data-driven approaches to model and predict soil-structure interactions or other complex geotechnical problems. Reliability-Based
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. Demonstrated high level of achievement in related research productivity and academic writing. Technical skills in computer programming, algorithm development and deep learning model implementation, and practical
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component disciplines; in explainable multi-modal deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in
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, funded by a Leverhulme Trust Research Leadership Award held by Dr Alessio Spurio Mancini. ECLIPSE's goal is to develop next-generation inference frameworks that combine machine learning with rigorous
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to evaluate pancreatic cancer pathology using human tissue specimens Assemble analysis pipelines using machine learning to process tissue data reproducibly and at scale Conduct analyses using programming
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, see the Geometric Machine Learning Group's website: https://weber.seas.harvard.edu For questions, please email mweber@seas.harvard.edu . Applications will be reviewed on a rolling basis. Basic
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. To learn more about the SCALES Postdoctoral Fellowship Program, visit our program page: https://climate.duke.edu/what-were-doing/scales-postdoctoral-fellows-program/ . Key Responsibilities Research
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profiling, and other cutting-edge, high-dimensional tissue analysis approaches to evaluate pancreatic cancer pathology using human tissue specimens Assemble analysis pipelines using machine learning
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, b) computational modelling, or c) machine learning. This is a complex project managing longitudinal data from a range of sources. Therefore, thoroughness and attention to detail while managing
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solving complex problems at the intersection of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical, IoT systems. Key