192 professor-computer-"https:"-"https:"-"https:"-"https:"-"FCiências" Fellowship positions in Australia
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recognition for quality research outputs in the field of biological mathematics. Demonstrated mathematics and computer programming skills with experience in modelling infectious disease dynamics, population
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+ 17% superannuation About the opportunity This position offers an opportunity to join a dynamic research team focused on multisectoral food systems policy and public health. Our research programme is
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opportunity for a Research Fellow (Level B) or Senior Research Fellow (Level C) to join the First Nations Cancer and Wellbeing Research (FNCWR) Program, led by Professor Gail Garvey within the School of Public
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% superannuation About the opportunity Funded through the extraordinary generosity of Professor Aola Richards, this fellowship supports one outstanding early-career researcher to lead an independent research
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School of Electrical Engineering and Computer Science Full-time, fixed-term position for up to 3 years Base salary will be in the range $104,209.38 - $111,431.10 + 17% Superannuation (Academic Level
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campus. This Level A research-only position will contribute to the research program of the Signalling Network Laboratory, undertaking projects focused on specific members of the protein kinase superfamily
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relationships in organic semiconductor materials, working closely with Professor Chris McNeill in the Department of Materials Science and Engineering, Dr. Amelia Liu in the School of Physics & Astronomy as
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, and evidence-based way. This position sits within the Public Health and Policy Pillar of Macquarie University Hearing, a strategic and growing research program led by Professor Bamini Gopinath, whose
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Australia’s Food and Beverage Accelerator’s (FaBA) Program : Based in the Queensland Alliance for Agriculture and Food Innovation (QAAFI) Full-time fixed-term position through to end of 2026 Base
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
machine-learning methods to investigate the deep-time controls on copper mineralisation. The role will involve developing reproducible computational workflows, generating predictive maps of copper