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Field
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/ Deep Learning Knowledge of: Active learning, Bayesian optimization Reinforcement learning or decision-making systems Experience with: Python ecosystem (PyTorch, Scikit-learn) Data pipelines and
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, Wallumattagal Campus, North Ryde The Role An exciting opportunity for a Postdoctoral Researcher exists in the School of Natural Sciences at Macquarie University. We are seeking a qualified candidate to join a
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, Computational Biology, Computer Science, Biomedical Engineering, or a related quantitative discipline Strong background in machine learning/deep learning (e.g., CNNs, transformers, vision models) Experience
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productivity, including technical presentations, abstracts, publications, or open - source projects. Experience with machine learning, computer vision, statistical analysis, or scientific software development
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. Education and scholarly development The postdoctoral associate will receive structured education in computer vision applications in medical imaging, machine learning, research methodology, responsible conduct
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-scale human datasets. You will: - Build and apply machine learning and deep learning models to multi-scale (cells, brains, patients), multi-modal (omics, biosensor data, vision, electronic health data
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, Interpretability and Transparency, Logics and Analysis, etc. Scientific Machine Learning: PDE Numerical Solutions, Dynamical systems, etc. Deep/Reinforcement/Deep Reinforcement Statistical Analysis and Tuning
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for accountability, transparency, inclusiveness, and misinformation. The key technology is multimodal deep learning, and its extensions for these additional targets. In particular, we have a large collection of
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career scientific staff Fieldwork experience Experience with public and private sector consultancy (or similar) We further wish to learn more about your visions and ideas for developing the research field
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Job Description Here's a Glimpse of the Job The Postdoctoral Research Associate will be involved in developing deep learning architecture for multi-object data integration, federated learning approaches