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focusing on the integration of machine learning, wafer-scale synthesis of materials. The role will contribute to the university's research mission by conducting fundamental research, helping secure external
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++. Demonstrable experience with machine learning frameworks (e.g., PyTorch, TensorFlow). Hands-on experience with game AI agents and/or GUI agents such as Mineflayer, Unity ML-Agents, or similar. Solid expertise in
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processing and statistical machine learning techniques to mine self-reports and sensor data to gain new insights towards assessment and longitudinal monitoring of bipolar disorder; Work on sleep datasets
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role in designing composites materials using inorganic solid electrolytes using computational modelling and machine learning. Qualifications • Ph.D. in Materials Science, Chemistry, Physics, or a
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to £43,805 per annum Apply by: 26/04/2026 Role Description We welcome applications from skilled, delivery-focused engineers to contribute to our successful railways-applied smart machines research programme
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Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical learning difficulties in kindergarten and early primary level students
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; - academic curriculum with background on the computer science and machine learning background; - previous research and professional experience on the scientific domains of the work. Additional Information
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on advancing AI technologies for space systems with significant national and global impact. Visit the AU website to learn more about the School of Computer and Information Technology . Our people Our people
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fields. You will be an integral member of an inter-disciplinary Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical
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, microbial cultures, and cleaning validation samples. Develop data analysis pipelines for Raman spectral classification, potentially integrating machine learning methods. Research & Project Responsibilities