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to develop and implement machine learning/deep learning tools for personalized medicine in cancer by exploiting electronic medical records and medical images in relation to cancer diagnosis and the
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and deep understanding of the global higher education landscape. The ability to thrive in a complex, fast-changing environment. The University of Leeds offers a range of benefits including excellent
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tasks and zero-shot evaluation in linguistic analysis. Profile • Master’s degree (M2) or PhD in computer science, NLP, machine learning, deep learning, or a related field. • Strong experience in machine
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perception systems, using deep learning and simulation-to-real domain adaptation techniques. You will work with a multidisciplinary team, contributing to fundamental and applied research. Your role will
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postdoctoral position, in collaboration with Demcon, focusing on off-road SLAM using lidar, camera, and IMU. Do you have a deep understanding of semantic segmentation, SLAM, sensor fusion (lidar, camera, IMU
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biologically-inspired deep learning and AI models (NeuroAI). The computational models we work with include vision deep learning models (including topographical deep neural networks), multimodal vision and
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https://www.academictransfer.com/en/jobs/357205/2x-phd-positions-in-the-mathema… Requirements Specific Requirements You have, or will shortly, acquire a Master's degree in either Mathematics
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/or spatial genomics, computational biology, machine learning, bioinformatics, and systems neuroscience. Prior experience with deep learning applied to biological data is a plus. Practical experience
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Doctoral Candidate in computer vision and machine learning for developing novel deep learning method
Machine Learning (DM3L) Doctoral Candidate in computer vision and machine learning for developing novel deep learning methods for satellite-based tracking of global CO2 and NOX emissions of point sources 80
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and run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures