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Field
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Learning-related field. Programming experience in Matlab, Python, C++ or other relevant language and experience in deep neural networks. Experience and demonstratable knowledge in deep learning, transformer
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technologies for humans. Researchers in MARCS come from many disciplines including cognitive science, developmental psychology, language science, music science, cognitive neuroscience, and biomedical, electrical
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research into the dynamics and prediction of the Western Central Pacific as part of the collaboration between the IMOS FishSOOP programme (run through UNSW) and the Pacific Community. The position will use
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peer-reviewed literature An interest in global change ecology, macroecology, biogeography, or conservation biology Strong computational and analytical programming skills A good knowledge of advanced
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Wildlife Crime Research Hub as part of the ARC Industry Laureate Fellowship program, Combatting Wildlife Crime and Preventing Environmental Harm at one of Australia’s leading research institutions
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programs, ensuring a rigorous foundation in quantitative disciplines while maintaining strong industry and research partnerships. About The Research Fellow Opportunity This is an exciting opportunity for a
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Science, Engineering or other Machine Learning-related field. Programming experience in MATLAB, Python, C++ or other relevant language and experience in deep neural networks. Experience and demonstratable knowledge in
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drafting and editing publications. Strong oral communication and interpersonal skills. Demonstrated experience with quantitative and/or qualitative research methods is desirable. A demonstrated knowledge of
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Application Deadline 7 Oct 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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biology and biochemistry. Fluency in written and spoken English, with an ability to communicate scientific ideas to an expert audience. A strong work ethic, and the ability to work well independently, and