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Field
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: Essential criteria A doctorate (or will shortly satisfy the requirements of a PhD). The doctorate must be in a relevant discipline area, such as statistical machine learning, computational and quantitative
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of hire. MD or PhD. Research expertise in Ophthalmology, Biomedical Engineering, Computer Science, Data Science, Machine Learning One year’s work experience, or equivalent in thesis research and other
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of the School of Electrical and Electronic Engineering in the areas of control and optimization, sensing and information processing, machine learning, cyber physical systems, robotics and automation, power
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representative work samples (e.g., journal paper, code repository, technical report, ontology/graph model) • 4 Reference letters, including one from candidate’s PhD supervisor • PhD/Master/Bachelor degree
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for machine learning and artificial intelligence, with a strong emphasis on developing and applying models such as LSTM and other time-series analyses to predict the longevity and behaviour of bioactive
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integration and optimized operation using machine learning and AI techniques as key drivers for improving system performance. The hired candidate will have the opportunity to work with cutting-edge energy
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Machine Learning. We are looking for a motivated candidate, who has interest in both theoretical, methodological and applied research in anomaly detection in sequential data settings, and who is excited
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: N/A Department: Elec, Computer & Bio Engr Campus Location: Job Description Summary: The search will remain open until the position has been filled
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focuses on scalable, AI-driven decision-making in collaboration with defence industry partners and an academic partner at the University of Melbourne. One role focuses on machine learning for decision
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neuroimaging, including substantial experience with EEG and/or fMRI data analysis and programming. Proficient in advanced neuroimaging data analysis, computational modeling, machine learning, and programming