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. This position offers an exceptional opportunity to conduct high quality research in the development of new transmission electron microscopy methods (STEM and/or TEM) and their application to solve important
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direction, produce high-quality publications and presentations, and supervise and mentor research staff and postgraduate students. By fostering interdisciplinary collaboration and strengthening links with
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performance of novel concrete Contribute to high-impact, multidisciplinary research Support environmental sustainability outcomes This is a great opportunity to develop your research expertise and contribute
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the Addiction & Impulsivity Research Lab and the Computational & Systems Neuroscience Lab . You will be part of a collaborative environment that integrates expertise in psychology, neuroscience and computational
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in the vibrant world of Indonesian history at Monash University's Faculty of Arts. Join one of Australia's most dynamic arts faculties, renowned for its strengths in humanities, performing arts
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molecular pharmacology, preferably GPCR-focused Solid understanding of receptor pharmacology Evidence of high-quality publications and independent research capabilities. Excellent communication and teamwork
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Research Fellow in Fractional Edge Decompositions of Graphs Job No.: 687653 Location: Clayton campus Employment Type: Full-time Duration: 2-year fixed-term appointment Remuneration: $83,280 - $113,025 pa Level A (plus 17% employer superannuation) Amplify your impact at a world top 50...
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international peers working on the program, industry and government stakeholders, and funding bodies. Exploring, leading and coordinating opportunities for new research proposals, initiatives, or collaborations
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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