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methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon
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and Applied Sciences Department/Area Electrical Engineering/Computer Engineering/Computer Science Position Description Project Deep learning plays an essential role in the operation of an autonomous
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substrates while advancing our understanding of deep learning through dynamical systems theory. You will work with two cutting-edge experimental systems: (1) light-controlled active particle ensembles
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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, approximate inference, deep learning, or Bayesian optimisation are encouraged to apply. Interpretable Machine Learning for Natural Language – Led by Prof Lexing Xie, this stream applies machine learning
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project “MORALITES: The Historical Positioning of Civil Society Leaders in National Moral Economies” led by Associate Prof. Anders Sevelsted at CBS. The project is running for 5 years until the end of 2028
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Qualifications: PhD in experimental particle physics at the time of appointment. Preferred: Deep understanding of the particle detectors, particle identification, data analysis Machine learning experience is a
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will engage in software development using deep learning, natural language processing, and large language models (LLMs). This position is embedded within a commercialization initiative focused on
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of biosystems and for extracting knowledge from (vast) sets of biotech data. A core technology leveraged by researchers at the center is deep machine learning, targeting the development of innovative tools and
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. This project is operated in collaboration with Carnegie Learning and Stanford University and is led by Principal Investigator Prof. Ken Koedinger. PLUS features a hybrid tutoring platform that combines human and