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field Strong background in control theory, optimisation-based algorithms and/or machine learning Excellent verbal and written communication skills Proficiency in programming languages in Python and/or C/C
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Partnership between UCL and AstraZeneca and to work as part of a cross-disciplinary team across both sites (London and Cambridge). This post is focused on the use of machine learning models of protein
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measurement quality issues related to respondent non-compliance in ecological momentary assessment or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models
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, emphasising our commitment to widening opportunity through the power of education and contributing our talents to creating a fairer world, and a more prosperous region. We are putting inspirational learning and
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quantum information theorists to integrate secure key rate calculations with simulations. Investigate and apply AI and machine learning techniques to improve and support secure-by-design development
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years of post-qualification experience at the time of application; (b) experience in using machine learning for research projects; and (c) have a good command of both written and spoken English
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sensing across telecommunications networks to deliver the performance level required by the multiple sensing applications. The applicants should have a solid theoretical background on machine learning
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Norwegian courses. Required selection criteria You must have completed a doctoral degree in (machine learning, statistics, or similar). You must have a professionally relevant background in algorithms
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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and sensor systems for remote maintenance and reliable fusion operations. Artificial Intelligence Developing advanced artificial intelligence/machine learning (AI/ML) solutions for fusion science and