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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and
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implementation of deep learning and computer vision frameworks across a range of research projects. This includes developing and training deep learning models for tasks such as scene understanding, object
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physics, applied mathematics, machine learning, bioinformatics, biophysics, spectroscopy, image processing, ecological modeling, molecular biology, plant physiology, marine biology or an interest in gaining
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areas of concerns to improve healthcare delivery to people with a learning disability and autistic people. We are contracted to deliver an annual report, regional reports and a number of deep dives as
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, Division of Applied Mathematical Science (Team Director; Eiryo Kawakami) (5) Medical Science Deep Learning Team , Division of Applied Mathematical Science (Team Director; Jun Seita) (6) Prediction
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relevant field at the PhD level with zero to five years of employment experience. Experience with deep learning frameworks (PyTorch, TensorFlow, JAX). Strong background in computational image processing and
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at least one year of postdoctoral research, have experience with data visualisation, and be enthusiastic about engaging with different online communities to learn more about public uses of the past. They
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communication and collaborative skills. Experience with SLAM, sensor fusion, LiDAR/depth camera data processing. Familiarity with deep learning for obstacle avoidance (e.g., map-less navigation). Background in
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different aspects of evolvability – the ability of organisms to evolve. We are interested in developing computational and mathematical tools to understand and quantify evolvability while exploring its
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the brain. We are particularly looking for a PhD level systems neuroscientist with expertise in animal behavior tracking using deep learning algorithms and its causal link with specific neural circuits