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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 investigate
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MACQUARIE UNIVERSITY - SYDNEY AUSTRALIA | North Ryde, New South Wales | Australia | about 8 hours ago
. 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 investigate
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Advance the theoretical foundations of machine learning, publish in premier venues and help shape the next generation of AI research. · Annual salary package: $104,481 (ACA Level A.4) - $112,550
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Doctor of Philosophy (PhD) in Machine Learning for In Situ Materials Characterisation Job No.: 698995 Location: Clayton campus Employment Type: Full-time Graduate Research Degree: 3291 - Doctor
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Computer Engineering at the University of Sydney is seeking to appoint a Postdoctoral Research Associate to contribute to an exciting project focused on the challenge of creating robust radio frequency
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. This study aims to construct a transparent, non-invasive predictive framework combining machine learning and explainable AI (XAI) to differentiate malignant from benign pelvic masses, stratify patient risk
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, demographic, lifestyle, laboratory, imaging and longitudinal health data provides opportunities to apply Artificial Intelligence (AI) and Machine Learning (ML) to colorectal cancer research. Research Aim
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. The increasing availability of electronic health records, clinical measurements, medical imaging and longitudinal health data provides opportunities for Artificial Intelligence (AI) and Machine Learning (ML
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for Artificial Intelligence (AI) and Machine Learning (ML) to support earlier and more personalised healthcare. Research Aim This PhD research aims to develop advanced AI and Machine Learning methods
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data provides opportunities for advanced Machine Learning (ML) approaches. Research Aim This PhD research aims to develop advanced Machine Learning methods for prediction, risk stratification, clinical