302 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" research jobs in Australia
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Intelligence or Machine Learning, with demonstrable analytical skills. Excellent research record evidenced by first-author publications in strong international journals and conferences. Proven experience in
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culture that encourages growth, innovation, and meaningful impact. About you You thrive in collaborative settings and are committed to fostering an inclusive and equitable learning and research environment
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& generous leave options. Salary packing options + 17% Super. EAP, fitness passport & discounted private health. Where to apply Website https://www.timeshighereducation.com/unijobs/listing/405620/research
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pm Where to apply Website https://www.timeshighereducation.com/unijobs/listing/405562/postdoctoral-fellow… Requirements Additional Information Work Location(s) Number of offers available1Company
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the success profile. For further information about this position, please contact Jan Radford, Professor in General Practice, j.radford@utas.edu.au or on 0419 885 285. Please visit https://www.utas.edu.au/jobs
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, rotating machines, system integration and electrical reticulation/protection. This role will also see you work collaboratively with a multidisciplinary team to advance renewable energy technology through
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on https://dfco2.org.au/research/ To be successful in this position, you will have: PhD in civil/mechanical/engineering or applied mathematics with research area related to theory or methods of reliability
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, measure transport and machine learning on developing a novel mathematical framework for identifying reduced dynamical models of high-dimensional complex multi-scale systems. The project will develop fast
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discover them The Opportunity The Department of Electrical and Computer Systems Engineering at Monash University is seeking a motivated Level A Research Fellow for a 2 year research-only appointment. A Level
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technologies will affect them. It is our anticipation that the work will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly