50 postdoc-computational-fluid-dynamics PhD positions at Monash University in Australia
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opportunities for a dynamic, ‘Learning Health System’ – where data can be harnessed to inform real-time and personalised decision-making. Existing linked administrative databases already capture Australian women
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field” imaging techniques to solve many important problems in biology and change clinical practice in respiratory medicine. Our ongoing research program involves developing new imaging technologies
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computing/ computer science, engineering, social science, science, community development). They will be committed to undertaking research that supports First Nations people and communities in accessing and
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nanomaterials" (with Prof Nikhil Medhekar) "Ultrafast charge dynamics in photoactive materials" "Artificial-intelligence-controlled atom-by-atom synthesis of functional organic nanomaterials" web page For further
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I am interested in the most catastrophic and explosive collisions in the Universe, such as the mergers of neutron stars and black holes. I study these using both gravitational waves and electromagnetic signatures, primarily focussed on linking the data from these exciting experiments with our...
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PhD Scholarship in Generative Artificial Intelligence Job No.: 686325 Location: Clayton campus Employment Type: Full-time Duration: 3.5-year fixed-term appointment Remuneration: The successful applicant will receive a Research Living Allowance, at current value of $36,063AUD per annum 2025...
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-term appointment Remuneration: The successful applicant will receive a tax-free stipend, at the current value of $36,063 per annum 2025 full-time rate, as per the Monash Research Training Program (RTP
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include: the evaluation of an existing health prevention program, the development of a measurement tool for health inequalities, behavioural experiments to assess how preventative interventions can improve
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Metallurgy and Corrosion cluster, working within a multidisciplinary team spanning theory, advanced characterisation, and computational modelling. This environment provides an excellent platform for developing
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, computational modelling, and data-driven alloy design to: Understand the mechanisms of local austenite-to-ferrite transformation in low-alloy steels; Develop frameworks to predict and control