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Research Fellow in Earth System modelling Job No.: 682250 Location: Clayton campus Employment Type: Full-time Duration: 3-year fixed-term appointment Remuneration: $83,280 - $113,025 pa Level A
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retention, models of care and healthy populations. These roles involve collaborating with a distributed team of rural health researchers to grow research capacity, attract strategic funding and deliver
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computational tools for quantifying electromagnetic field distributions down to the fundamental atomic scale. The project will build on recent developments in inverse scattering methods, including ptychography
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outputs of a team dedicated to translating discovery into meaningful impact for people living with Friedreich Ataxia. We are seeking someone with a PhD in computer engineering, biomedical engineering
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: Developing and deploying machine learning models (e.g. graph neural networks, neural force fields, diffusion models) for molecular property prediction and molecular generation. Integrating quantum chemistry
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discover them Shape the future of critical-care research through data innovation. Monash University’s School of Public Health and Preventive Medicine (SPHPM) is at the forefront of improving health and
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evaluate methods via experiments, benchmarking, simulation and/or real‑world data. The successful candidate will have: A PhD in Statistics, Data Science, Computer Science, Mathematics, or a related field
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. This is a unique opportunity to contribute to the global transition to net zero energy by developing and applying cutting-edge tools for autonomous materials discovery. You’ll work within the Australian
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into neurocognitive outcomes The Opportunity This is an exciting opportunity to join a dynamic research team advancing innovation in addiction and mental health treatment. As a Research Fellow, you will contribute to a
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, fabricate structures at the Melbourne Centre for Nanofabrication, and measure their optical and electrical properties. The successful candidate will have a PhD in Physics, Materials Engineering, or a closely