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Machine Learning, Human-Computing Interactions, Social Sciences, and Public Health. Applicants should hold, or be close to completion of, PhD/DPhil with research experience in computer science, statistics
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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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: Statistical signal/image processing, deep learning, machine learning, neuromorphic computing Good communication skills and an appropriate publication record are essential. Solid knowledge of Python and C++ is
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-electron Schrödinger equation for fermions and bosons with high accuracy and on the application of these methods to problems in the physics of oxides, semiconductors and their surfaces. Machine learning
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students and PhD students. Applicants will have, or be close to completing a PhD in a relevant field and possess relevant experience, in the area of probability or statistical machine learning. They will
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain
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students and PhD students. Applicants will have, or be close to completing a PhD in a relevant field and possess relevant experience, in the area of probability or statistical machine learning. They will
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working with digital signal processing, advanced filtering techniques, dynamic feature extraction, time-domain and frequency-domain analysis, signal fusion and machine learning to enhance Crainio system
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projects in computer vision research, with a particular emphasis on Spatial Intelligence, 3D Computer Vision, and 3D Generative AI. You should hold a relevant PhD/DPhil (or near completion*) in Computer
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discipline (eg Statistics, Machine Learning, Biostatistics, AI, Engineering) with experience of developing and applying new methods. You will be able to develop research projects, with publications in peer