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Machine Learning without Centralized Training Data”, https://ai.googleblog.com/2017/04/federated-learning-collaborative.html [2] “Learning with Privacy at Scale”, https://machinelearning.apple.com/research
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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find some of our publications here: https://i.giwebb.com/research/computational-biology/ Required knowledge A solid grounding in artificial intelligence and machine learning. Learn more about minimum
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machines, system integration and electrical reticulation/protection. This role will also see you work collaboratively with a multidisciplinary team to advance renewable energy technology through cutting-edge
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mechanical loading of such samples. The focus of the PhD project will be to use machine learning techniques to better understand the interplay between the crystal orientations and deformation patterns in a
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group of PhD researchers who will tackle the most pressing questions in Machine Learning while ensuring AI serves humanity responsibly. You'll work within one of our specialised research themes, each
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(Placement Partnering & Operations) team and help students access high‑quality work‑based learning experiences that strengthen our connection with local communities and industry. Working within a portfolio
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contributing to applied data science and machine learning activities that support urban analytics and research outcomes. The role focuses on reviewing, improving, and maintaining an existing data processing
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and passionate individuals who share our values and vision. RMIT University has a global reputation and ranks 125th globally and 10th in Australia in the QS World University Rankings 2026. To learn more
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high‑quality work‑based learning experiences that strengthen our connection with local communities and industry. Working within a portfolio team under the direction of the Business Partner & Team