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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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Martin Australia invite applications for a project under this program, advancing robotic perception systems through monitoring of their machine learning models. Run-Time Monitoring of Machine Learning
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interested in connecting spatial and spectral information to understand complex materials systems at the molecular level with machine learning. PhD Student A will work with tumour sections to develop multiple
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catastrophically so. This PhD will develop technologies for addressing this serious problem, building upon our groundbreaking research into the problem. Required knowledge A solid grounding in machine learning
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experience with programming (e.g., Python), machine learning, or educational data is beneficial, it is not a strict requirement. The project provides ample opportunities to develop these skills over time. What
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awardee. The Opportunity Generative artificial intelligence is a significant and highly visible use of machine learning which has become commonplace in a matter of a few short years. Without common
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to $5000 (total) for computer / software / conference attendance to present PhD results. Scholarship Details Maximum number awarded 1 Eligible courses Higher degree by Research (PhD) – in Health Sciences
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. • be located at the agreed project location(s) and, if required, comply with the university’s external enrolment procedures. Selection criteria Skillset: Proficient in Python, machine learning, and
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geometry, and/or data science. Specific topics of focus include, but are not limited to, linear response, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics
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a focus on neurological disease and neuroimaging. To be successful you will need: A PhD in Computer Science, Engineering or other Machine Learning-related technical field. Programming experience in