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Field
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should have required qualifications and experience Education: PhD in Biomedical Engineering, Electronic Engineering, Mechanical Engineering, Materials Science, or related disciplines with a strong academic
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contributions to musculoskeletal mechanobiology and regeneration program, specifically focused on using in vivo optogenetic and transgenic tools to understand developmental processes, mechanical adaptation
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. This position is best suited for candidates with a strong background in computational material science. The role reports to Associate Professor Patrick Burr and has no direct reports. Salary (Level A) – AUD
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household-level impacts. Simulate the effects of policy scenarios (e.g., shifts from standing charges to alternative pricing mechanisms) on different archetypes, analysing implications for energy
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PhD Research Fellowships: Artificial Intelligence Adoption, Sustainable Finance, and Twin Transition
, Sustainable Finance, and Twin Transition Apply for this job See advertisement About the positions Inland School of Business and Social Sciences , The PhD programme Innovation in Services in the Public and
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employment period is three years. A premise for employment is that the PhD Research Fellow will be enrolled in USN's PhD-program in Technology within three months after accession. About the PhD-project
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of the UCL Queen Square Institute of Neurology (ION). The Centre combines laboratory and clinical expertise focused on neuromuscular and neurological disorders, their mechanisms of disease and includes cutting
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-integrated optical system capable of probing tissue mechanics at unprecedented speeds, with direct implications for early cancer diagnostics and beyond. Key Responsibilities: • Design and develop fibre
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and the School of Computing Technologies to advance research on bushfire evacuation and emergency planning and preparedness in hazard events. You will support the research aim and objectives of the ARC
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computational simulations in the area of density functional theory (DFT). The role will focus on understanding and predicting the properties of novel functional materials, including surfaces and interfaces