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machine-learning and AI methods for complex engineering and industrial systems, with a particular focus on improving their reliability, availability, and operational performance while enabling more
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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: PhD degree in Computer Science, Computer & Electronics Engineering or other related fields Strong background and knowledge in at least one or preferably more of the following fields: Cybersecurity, Deep
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integrating power electronic converters and electrical machines we can use common structures and systems to greatly reduce, material usage and energy consumption. Through a multidisciplinary research approach
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dedicated researcher with a PhD in mathematics, electrical or mechanical engineering, computer science or a related field, and a strong methodological background in machine learning. The ideal candidate has
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and Computer Engineering School of Project Management All positions are full-time, continuing (tenure-track), teaching and research roles based in Sydney. Successful applicants will join a group of
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doctoral degree Collect, structure and assess relevant sensor, operational, maintenance, incident and cost data Develop and validate statistical, causal and/or machine-learning methods and turn the results
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PDRA, who will lead complementary feature-engineering research; investigate how engineered physiological features can be combined with learned representations from the Sheffield machine-learning work
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will focus on efficient probabilistic analysis of high-dimensional and dynamic systems, including advanced sampling, surrogate modelling, and AI or machine-learning methods where appropriate. Key
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join a highly collaborative, interdisciplinary research environment focused on developing and applying cutting-edge machine learning and deep learning methods to abdominal imaging. Working alongside