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and Prof Kath Hulse (Swinburne). This PhD project will analyse the role and mechanisms of social communication, learning and social networks in fostering sustainable and energy efficient household
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on principles from engineering, cognitive neuroscience, artificial intelligence, and human-computer interaction. It has potential applications in education, training, and cognitive rehabilitation, and contributes
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closed-loop control. The technology, developed through detailed computer simulations, will be validated with preclinical experiments. The candidate will be part of a multidisciplinary team working towards
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mathematics, (theoretical) computer science, machine learning foundations, electrical engineering, information theory, cryptography, statistics or a related field. - Advanced knowledge of probability theory
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disciplines: computer science/engineering or biomedical engineering. You have an excellent academic record of accomplishment. In particular, you have a good command of, and a strong interest in, computer
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the Department of Developmental, Personality, and Social Psychology (with Prof. Dr. Matteo Giletta and Dr. Nathalie Michels as supervisors; Phaselab) and the Department of Head and Skin (with Prof. Dr
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networks and their demonstration as proof-of-concept implementation in an experimental 6G testbed. Your qualifications MSc in Computer Science or Electrical Engineering Strong background in networking and
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for focal epilepsy with ultrasound neurorecording, modulation, and deep reinforcement learning (DRL) closed-loop control. The technology will be developed through detailed computer simulations and preclinical
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able to experience hands-on testing of a high-speed and engine-representative compressor further increases of the efficiency and stability margin. The project is highly innovative, will generate
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must have (or will receive in a few months) a Master’s degree in Computer Science Engineering, Master of Science in Information Engineering Technology, Master of Science in Computer Science, Master of