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Research Fellow in Computational Neuroscience Job No.: 686523 Location: Clayton campus Employment Type: Part-time, fraction (0.7) Duration: Fixed-term appointment until 30 November 2026 Remuneration
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We are looking for a Research Fellow who will contribute
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technologies will affect them. It is our anticipation that the work will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly
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of Women’s Health program outcomes through a range of laboratory and technical activities, including sample preparation, cell and tissue culture and performing assays such as qRT-PCR, immunoblotting
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program that advances the understanding within the scientific fields of brain and mental health. This position will contribute to the research priorities of the Turner Institute for Brain and Mental Health
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collaboratively with colleagues, strong computer literacy with proficiency in Microsoft Office and relevant university systems, and the ability to learn new software as required. Experience working with schools
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Pro Vice-Chancellor (Researcher Development) Job No: 688391 Location: Clayton Campus Employment Type: Full-time, Fixed-term appointment Remuneration: A competitive remuneration package will apply Champion the next generation of researchers and research excellence at one of the world’s leading...
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across academia and industry. This position forms part of a major national program in Australian green ironmaking and will utilise Monash’s world-class analytical capabilities and pilot-scale facilities
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. We are currently seeking a Research Fellow with experience in AI and machine learning research and development, with a focus on any or all of following application areas: Computer vision Generative AI
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the guidance of artificial intelligence techniques. The project will develop novel design processes that embed material behaviour within agent-based and machine learning computational design systems