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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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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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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
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., multispectral imaging) and computational tools such as machine learning or AI into research workflows. Strong project management skills, including planning, milestone delivery, and working with industry partners
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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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offering solutions to complex problems and creating new possibilities. At La Trobe, our students gain essential skills in programming, data analysis, and machine learning, equipping them to shape the future
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components. The resulting data will be used to train a machine learning (ML) model, enabling automated and efficient beamline alignment. This technology has the potential to significantly enhance
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the world. Ideal applicants will have a solid background in AI, machine learning, control theory or quantitative finance. Applicants with advanced programming skills (Python/C++); and a desire to publish in