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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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, attention-based models, and multi-modal learning approaches—to model RNA-mediated regulatory mechanisms and their dynamic interactions in disease. The position is part of an NHMRC Ideas Grant project
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to deploy soft sensors in real-time environments. Key responsibilities will include: Under the guidance of senior researchers, program process models for the minerals processing industry Assist with
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Postdoctoral Research Associate in Global Environment Modelling of Soil Organic and Inorganic Carbon
. The project is aimed to improve our in-house developed process-based computer model and use it to represent the soil ecohydrological and biogeochemical interactions across various carbon and nitrogen soil pools
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30 Aug 2025 Job Information Organisation/Company UNIVERSITY OF SYDNEY Research Field Computer science Researcher Profile Leading Researcher (R4) Country Australia Application Deadline 14 Sep 2025
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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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modelling and simulation of complex systems reinforcement learning or graph neural networks proficiency in Python and related computational toolchains a strong interest in interdisciplinary research bridging
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Computer Engineering at the University of Sydney is seeking to appoint a Research Associate or Postdoctoral Research Associate to contribute to an exciting project focused on the challenge of accelerating
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successful in this position, you’ll have: A Master or Honours degree in Computer Science related field Demonstrated expertise in ML, with significant experience in designing and implementing large-scale models
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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