295 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" research jobs in Australia
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within the Faculty of Engineering, currently with over 1500 students and 43 academic staff, with an operating budget of around $18 million. For further information about our School, please visit - https
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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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analytics More information can be found at https://smi.uq.edu.au/mishc About This Opportunity This is an exciting opportunity for a Professorial Research Fellow to work within the Minerals Industry Safety and
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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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econometrics and machine learning, is highly desirable. You are well-organised and capable of managing multiple tasks to meet deadlines, with excellent written and verbal communication skills for producing clear
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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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will involve identifying, sourcing, and installing various sensing equipment in Rux’s in micropilot plant. The data from these sensors will be integrated into an existing platform composed of open-source
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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