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process, including downstream effects on other clinicians, patients, information flow and safety. This PhD project will examine the system-level impact of health AI, using human factors and systems
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to provide specialist technical expertise within its Analytics team. Working in an innovative research environment supporting discovery, bioprocessing (upstream and downstream) and quality assurance, you will
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mRNA production techniques, in particular downstream processing such as HPLC, FPLC and TFF. Demonstrated knowledge in RNA analytical techniques. Knowledge and experience working with and troubleshooting
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of foundation models in natural language processing and computer vision, this project seeks to develop general-purpose graph foundation models capable of learning transferable representations from large-scale
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and downstream processing. Upstream responsibilities include media and reagent preparation, cell culture operations, scale-up to stirred-tank bioreactors, and filtration processes. Downstream
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mapping, data flows, databases and integrated reporting solutions, and can assess the downstream impacts of system and process changes. Key skills include: Business analysis and technical project
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and process complex longitudinal clinical and MRI datasets, including data extraction, organisation, quality control, and preparation for downstream analysis. Execute and maintain MRI preprocessing and
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stakeholders, application owners, architects, delivery partners and technical teams, the role defines how business processes, information and transactions flow between Oracle Fusion and UNSW’s broader
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, supporting advanced microscopy workflows and the downstream analysis of research samples. You will promote best-practice histological techniques, provide training and expert guidance to facility users, and
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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions