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the project's industry partner. The School of Physics is committed to creating a diverse workplace by improving equity, access and opportunity. We want to create an environment of world-leading teaching and
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digital intervention to improve youth mental health. In this role you will coordinate research activities and a randomised controlled trial, funded by the Medical Research Future Fund, for a social
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25 Feb 2026 Job Information Organisation/Company UNIVERSITY OF SYDNEY Research Field Physics Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Application Deadline 29 Mar
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Full time, fixed term for 3 years. Located on the Camperdown Campus at the School of Physics Collaborate with industry partner Cortical Labs Base Salary Academic Level A, $109,263 - $121,054 + 17
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when you thrive, we thrive. We are transforming education for contemporary learners and global citizens. Building on a proud legacy and shaped by bold ambition, it’s a place of excellence and equity
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MACQUARIE UNIVERSITY - SYDNEY AUSTRALIA | North Ryde, New South Wales | Australia | about 1 month ago
experience in: Radial velocity, stellar, and/or exoplanet spectroscopy studies; and/or astronomical instrumentation or observing programmes Machine learning in physical science, using Jax, PyTorch, and/or
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activities and a randomised controlled trial, funded by the Medical Research Future Fund, for a social connection intervention to improve adolescent mental health (the Mind Your Mate program). You will work
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Multiple, 2-year or 3- year full time, fixed term positions, located on the Camperdown Campus at the School of Physics Opportunity to make valuable contributions to research in single junction and
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Service: Contribute to the academic community through internal service, collaboration with industry and external partners, and participation in activities that support the School and University. This is a
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seeking applicants with experience in: Radial velocity, stellar, and/or exoplanet spectroscopy studies; and/or astronomical instrumentation or observing programmes Machine learning in physical science