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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 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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comparison to my peers. I am proficient in written and spoken English. During my courses or prior professional activities, I have gathered experience with machine/deep learning, and can demonstrate a strong
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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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conducting experiments for training and evaluating deep neural networks Knowledge of multi-modal learning, transfer learning, transformers, or self-supervised learning Experience in dealing with large medical
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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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solutions and applying them in real-world scenarios. Proficiency with machine learning frameworks and pipelines in SKLearn, Numpy, Pandas, and PyTorch. Proficiency with deep learning frameworks such as
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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
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the creation of high-precision digital twins. Activity 1: Integration of Photometric Stereo in Meshroom - Implement processing nodes for normal field and intrinsic color estimation. - Integrate deep learning