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analysed by bespoke machine-learning driven algorithms, combined with physical models, to de-noise images, identify features and correlate properties, giving critical insights into power loss pathways
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emerging spectrum (FR3, MMW, sub-THz/THz) Extreme massive MIMO communications Analog, digital and hybrid beamforming architectures Reconfigurable intelligent surfaces Machine learning for wireless
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(SHORES) and the Division of Engineering, New York University Abu Dhabi, seek to recruit a Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital
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approaches and will integrate novel hardware (including electrode arrays, microdevices, analytical systems) into automated robotic pipelines You will also apply machine learning-based analyses to imaging and
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for candidates to have the following skills and experience: Essential criteria PhD qualified in mathematical, physical or computational sciences Experience in using machine learning methods to analyse datasets
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experience with machine learning frameworks such as PyTorch or scikit-learn. You are a team player and a team leader: you can mentor PhD students and Bachelor students effectively. You share your knowledge
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(KCL, London, UK) but will also have the opportunity to travel and work at the Centre for AI and Machine Learning (ECU, Perth, AU) and the School of Psychiatry and Clinical Neuroscience (UWA, Perth, AU
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the outcomes of SCC surgery. Job Responsibilities: As a PhD candidate, you'll focus on: Develop cutting-edge AI models: Train state-of-the-art deep learning models to segment SCC and healthy tissues using both
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patent filings. The work will be centred around topics such as machine learning for communications, communication theory, signal processing for communications, coding theory, and information theory. Your
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comes with no teaching obligations BASIC RESPONSIBILITIES AND OBLIGATIONS Conducting research related to the scientific project titled “Calculus of variations in machine learning problems”, in particular