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Research Fellow - Environmental Informatics Hub Job No.: 680160 Location: Clayton campus Employment Type: Full-time Duration: 2 year fixed-term appointment (with the possibility of an additional 2
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modelling, and the economics of mental health Support economic analysis of healthcare utilisation, outcomes and costs using national datasets Develop and apply algorithms for cost-effectiveness analysis
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people who discover them The Opportunity The Department of Electrical and Computer Systems Engineering is seeking applications for a Level A Research Fellow to contribute to a high-impact research project
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, and may utilise iterative algorithms, machine learning and high-performance computing. Through the Monash Centre for Electron Microscopy, opportunities exist to acquire large experimental datasets using
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group of experts to predict (probabilistically) whether these occupations will be automated, augmented or unaffected by emerging technologies. Using this data, a classification algorithm is then trained
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the Addiction & Impulsivity Research Lab and the Computational & Systems Neuroscience Lab . You will be part of a collaborative environment that integrates expertise in psychology, neuroscience and computational
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and beyond. About You We're seeking a passionate individual who thrives both independently in a research environment and as a collaborative member of the History program. As the successful candidate you
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Join Monash University’s cutting‑edge mathematical research in the Faculty of Science as a Research Fellow and contribute to computational and theoretical aspects of Dynamical Systems. This position will
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of innovative computational procedures and methodologies, addressing a national skills shortage and enabling timely progress on a high-impact research initiative in modern econometric modelling. The role provides
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quantification This is a unique opportunity to contribute to an internationally recognised research program and shape the future of materials modelling and mechanical engineering. You’ll be part of a supportive