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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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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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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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possibilities. At La Trobe, you’ll gain essential skills in programming, machine learning and data analysis, preparing you to lead innovation in areas such as digital healthcare, smart manufacturing, sustainable
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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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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
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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