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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 10 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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fingerprinting (RFF) systems using machine learning. The successful candidate will have significant experience in signal processing or machine learning, and an outstanding track record in conducting research. You
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of creating robust radio frequency fingerprinting (RFF) systems using machine learning. The successful candidate will have significant experience in signal processing or machine learning, and an outstanding
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Research Fellow – AI/Machine Learning in Pharmaceutical Data Job No.: 697007 Location: Clayton campus Employment Type: Full-time Duration: 11-month fixed-term appointment Remuneration: $86,195
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for Academic Performance . About You You will demonstrate: Completion of a PhD in computer science, cyber security, human-computer interaction, or a closely related discipline with a focus on privacy, provenance
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passion for applying advanced machine learning to real-world medical challenges. If you thrive in collaborative, multi-disciplinary environments and possess a strong technical foundation in AI methodologies
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machine learning can be used to understand complex behaviours such as reliability, competence, transparency and trust. The position will design and develop innovative reasoning algorithms and knowledge
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materials discovery with cutting-edge high-throughput platforms, robotics, machine learning, and autonomous experimental workflows. Access World-Class Facilities: Based in the Department of Chemical and
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analyse electrochemical free-energy landscapes and connect elementary-step energetics to catalytic performance apply machine-learning interatomic potentials and microkinetic models where appropriate