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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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will likely have the following skills and experience: A PhD or master’s degree (or commensurate qualification/research experience) in Computer Science, Data Science, Forensic Science, Electrical
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to meet deadlines and effectively manage varying workloads and respond to changing priorities as required Demonstrated high level of communication skills Demonstrated hands-on experience in interface
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high-impact research at the interface of seafood safety, regulation, and public health Full time, ongoing role based in Hobart About the opportunity This is a senior, research-intensive position within
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will work as part of a collaborative team, working closely with fellow postdoctoral researchers and PhD candidates across ARC Research Hub for Molecular Biosensors at Point-of-Use (MOBIUS) and other
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interfaces set optical loss - Fabrication of waveguides and ring resonators from these glass layers - Optical characterisation of loss, scattering and resonator finesse A core part of this role will involve
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decision-making on Total Allowable Catch (TAC) and regional harvest strategies. This position operates at the interface of science, policy, and industry, translating complex ecological and statistical
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an operating budget of around $18 million. For further information about our School, please visit - https://www.unsw.edu.au/engineering/our-schools/electrical-engineering-telecommunications Skills & Experience