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of experiments, analysis of neuropsychological and cognitive data and application of computational models. It also contributes to scientific publications and supports collaboration within a network of researchers
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people who discover them The Opportunity The Research Fellow will be a motivated and talented computational scientist and a key member of Monash University ice sheet modelling research team and SAEF’s
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science, computational chemistry, computational biology, or a closely related discipline. Demonstrated experience in the design of machine learning architectures such as GNNs, VAEs, or diffusion models applied
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programming (R or Stata preferred), and scientific computing Excellent problem-solving, planning and written communication skills Experience working with health-related data and modelling methods A
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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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projects involving multi-electrode array recordings in behaving animals, using techniques including electrophysiology, psychophysics, computational modelling and advanced quantitative analysis. This position
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. Working closely with the ABI program’s leadership team, you’ll contribute to data curation, protocol development, predictive modelling, and novel analytics for large-scale neurocritical care datasets. As
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within the Computational Neuroscience Laboratory and the Addiction and Impulsivity Research Group , this position offers the opportunity to lead all aspects of trial preparation and execution, including
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focused on advancing personalised and innovative approaches to addiction care. This dynamic and collaborative position works closely with a multidisciplinary team of neuroimagers, computational modellers
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COVID-19 coronavirus and we seek a sustainable path forward. We collect a variety of human opinions to help model and predict the various changes in various parts of the workforce. This will assist in