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more effective screening and therapy. The postholder will focus on developing and applying advanced computer vision and machine learning methods for multimodal imaging and real-time analysis in
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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles
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intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet
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. DVXplorer), and tactile/force sensors. Strong background in computer vision and deep learning, with practical implementation experience. Proficiency in programming with C++ and Python, including use of ROS
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-based cameras (e.g. DVXplorer), and tactile/force sensors. 3. Strong background in computer vision and deep learning, with practical implementation experience. 4. Proficiency in programming with
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., knowledge representation and reasoning) and bottom-up (e.g., machine learning) methods to study the representation of geographic categories and processes. While we welcome applicants from a broad range of
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2025. We seek to recruit a Research Associate specialising in statistical modelling and machine learning to join our multi-university multi-disciplinary team developing a groundbreaking technique based
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climate scientists and artificial intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine
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experience in machine learning and image analysis for ultrasound images and video. The successful applicant will possess specialist experience conducting fieldwork, particularly in low-resource or rural
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properties of Li-rich three-dimensional materials for lithium battery cathodes using density functional theory (DFT), molecular dynamics, cluster expansion, machine learning computational techniques. This work