34 high-performance-computing-postdoc Fellowship positions at Monash University in Australia
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, to undertake further research leading to high-quality publications and conference outputs.The position involves active participation in academic events, limited administrative duties, occasional teaching
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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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an interdisciplinary, purpose-driven team. You have: A postgraduate qualification in Computer Science, Data Science or related field Extensive experience working with large-scale, high-frequency (waveform) data
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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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for eating disorders. This position offers the opportunity to lead a multidisciplinary clinical research team in the delivery and dissemination of high-quality clinical trials co-designed with people with
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sits at the forefront of an international program aimed at improving the understanding and treatment of alcohol use disorder. This position will leads functional neuroimaging research and plays a key
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meaningful program of work that includes literature reviews, field research, data analysis and scientific writing. The role also involves preparing ethics applications and presenting research findings
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proposals. It also involves supervising research support staff and mentoring junior academic colleagues, helping to foster a collaborative and high-performing research environment. A doctoral qualification in
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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