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equipped with state-of-the-art research infrastructure, housing a comprehensive range of cluster laboratories, test bedding facilities, research centers/institutes and corporate laboratories. Cutting-edge
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adaptive trials, stepped wedge trials and cluster trials. You will have good knowledge of applied statistics and statistical theory, especially for pragmatic trials and complex randomisation systems. You
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of classes, using Machine Learning (ML) techniques such as Decision Trees, K-Nearest Neighbors (KNN), XGBoost, Support Vector Machines (SVM), or Neural Networks. Explore and implement clustering algorithms
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 4 days ago
cluster. The successful candidate(s) will contribute to collaborative research, educational activities, and stakeholder engagement, while actively participating in the broader intellectual and cultural life
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. Qualifications/Requirements Qualifications / Discipline: - PhD from a reputable institution in Physics, Bio-imaging, Computer Science, or a scientific domain closely related to Machine Learning. - The candidate
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in utilizing machine learning libraries such as PyTorch, TensorFlow, and Scikit-learn. At least 2 years of experience working with Linux computing clusters. Ability to work independently and within a
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 1 day ago
be based in the School of Medicine and Psychology, with strong links to the School of Computing within the Data Science and Analytics cluster. The successful candidate(s) will contribute
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adaptive trials, stepped wedge trials and cluster trials. You will have good knowledge of applied statistics and statistical theory, especially for pragmatic trials and complex randomisation systems. You
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activity and research after achieving the PhD or other doctoral degree under the direction of a senior faculty member who serves as a mentor for the postdoctoral appointee. Additional Department Summary
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implementation science. We are now seeking an exceptional Research Fellow (Level B or C) to lead innovative methodological research in statistical theory for adaptive variants of longitudinal cluster randomised