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communication and patient intake processes, which may introduce bias in differential diagnoses and disease classification. As part of the project, we aim to develop a machine learning pipeline that will be able
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. This project focuses on building confidence in ASL when coupled with our advanced machine learning tools for clinical application in dementia. Supervisor: Prof Michael Chappell Eligibility: https
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sensor data. Supervisor Bio Dr. Matthew Ellis’ research intersects machine learning and physics; looking to better integrate advances in both to create new paradigms for computing. With a background in
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University of Louisville Department of Civil and Environmental Engineering | Louisville, Kentucky | United States | 4 days ago
of hydrologic, sediment, and nutrient transport at variable spatiotemporal scales. Dr. Mahoney’s lab utilizes process-based numerical models, machine learning, and sensing/tracing technologies to investigate
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of Manchester from October 2024. The use of machine learning methods and molecular simulations for polymer design and property prediction is the new frontier in polymer science. This project aims at using a mix
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modelling tools and machine learning methods. The project brings together world-leading experts in both academia and industry, across fields including superalloy metallurgy, microstructure characterisation
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Jobs PhD scholarship in Machine Learning Techniques for Spectral Shaping of Ultra-Broadband Optical Frequency Combs - DTU Electro Kgs. Lyngby, Denmark Posted on 05/01/2024 DTU Electro invites
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meaningful features from these sensor data and apply machine learning algorithms to predict health outcomes; 2) to explore advanced deep learning methodologies to further exploit the information embedded
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University of Georgia Warnell School of Forestry and Natural Resources | Athens, Georgia | United States | about 2 months ago
Job Type: PhD Research Assistantship (towards a PhD degree in Water Resources) Project: TEAROOM (Tracer-EnABled hydRolOgic Modeling) Research Focus: Utilizing tracer data for improving hydrologic