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Research Fellow (Machine Learning LEO Satellite Communications) Employer MACQUARIE UNIVERSITY - SYDNEY AUSTRALIA Location North Ryde, Australia Salary $112,550 - $120,621 (Level A6-A8) per annum
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. The position contributes to “Brain-in-Space: A Machine Learning Framework for Self-Organizing Low-Earth Orbit Satellite Networks”, funded by the Global Science and Technology Diplomacy Fund. You will
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. We are particularly interested in: machine learning for molecular and omics data, including representation learning for biological sequences and structures, and the integration of multiple omics
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and machine learning analyses to uncover trajectories and predictors in young people. You will contribute to high-impact publications and global collaborations across NIH, MRFF and CRE-funded projects
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cutting-edge bioinformatics, AI, machine learning, and multi-omics technologies to drive discoveries that have the potential to transform cancer diagnosis, treatment, and patient outcomes. This is an
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particularly interested in: machine learning for molecular and omics data, including representation learning for biological sequences and structures, and the integration of multiple omics layers machine
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apply mixed-model statistical methodology, including genomic prediction, crop simulation and machine learning, to improve prediction of crop yield and related traits. Supervision and Researcher
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management systems, workflows and research infrastructure. Apply advanced statistical, machine learning and data engineering methodologies to large-scale, longitudinal datasets, contributing to
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Australian National University | Canberra, Australian Capital Territory | Australia | about 8 hours ago
knowledge and experience in computing, algorithm development, computer simulation, and/or artificial intelligence and machine learning; has excellent communication skills and a demonstrated ability to
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vision or machine learning methods to biological data. Applicants should have good working knowledge of experimental design for welfare science and an interest in translating research into deployable