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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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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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interaction and human motion analysis Prior knowledge of machine learning/deep learning applied to motion analysis (e.g., relevant courses and research experience) would be an advantage IELTS score of 6.5
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. Specific projects seeking applications are: Accelerating the discovery of inorganic solar-cell materials via a closed-loop, fully robotic synthesis–characterisation platform driven by multi-agent machine
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external enrolment procedures. Selection criteria Demonstrated experience in programming and system development. Expertise in Python programming and data analysis. Experience developing Machine Learning
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and hands-on experience with AI and computer vision. Solid programming skills in Python, especially with PyTorch. Practical experience with deep learning projects, including working with attention
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affect surface outcomes, benchmark against conventional techniques, and evaluate performance of the finished components. You’ll also delve into intelligent automation and machine learning to optimise
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factors involved in the onset and progression of dementia. Advanced computational methods, including bioinformatics pipelines and machine learning, will be employed to uncover putative biomarkers and