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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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. This process operates under ambient conditions, utilises water as a hydrogen source, and is powered by renewable electricity. Recent advances in electrocatalyst design – enabled by operando spectroscopic
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PhD Scholarship - Movement Metrics for Physical Health Job No.: 682825 Location: Clayton Campus Employment Type: Full-time Duration: 3.5-year fixed-term appointment Remuneration: The successful
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to: Domestic students PhD Eligibility Criteria The student will have a Bachelor's degree in science (ideally physics) or engineering (ideally electrical), plus a strong aptitude for technology and a desire to
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the industry partner) Scholarship Details Maximum number awarded 1 Eligible courses Degree in Chemical Engineering or Biochemical Engineering or other relevant degree Eligibility criteria To be eligible, PhD
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Status: Closed Applications open: 1/09/2024 Applications close: 30/09/2024 View printable version [.pdf] About this scholarship Description/Applicant information The MRIWA Odwyn Jones PhD
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Australian Research Council (ARC) Funded PhD Opportunity at Faculty of Engineering: High-Speed Rail and Sustainable City Sizes in Australia Location: Clayton campus Department/Unit: Monash Institute
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are deeply committed to environmental stewardship and eager to apply their specialized skills in AI, data analysis, software development, and electrical or energy engineering to make a significant and tangible
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bring: A PhD, or an equivalent combination of qualifications and demonstrated research experience, in Electrical Engineering, Mathematics or related fields, with a focus on power systems engineering