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behaviour testing of steel and aluminium members/connections iii. Fabrication trials and validation of welded structural components iv. Data acquisition, instrumentation and monitoring during laboratory
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5, with a particular emphasis on empirical analysis, AI-enabled data construction, and policy-relevant research outputs. Duties and Responsibilities: Design, develop, and execute empirical research
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and conduct field measurements of lift traction motor noise and vibration in residential buildings using sound intensity probes, accelerometers, and multi-channel data acquisition systems. ii. Perform
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national and international projects. About the opportunity Lead project field data science related to quantifying water security and biodiversity. Deliver outreach and adoption that translate research onto
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information/data from one structure can be used to manage similar structures from the same population. New machine learning, sensing and digital twin technologies will be developed with the aim of driving new
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nanopore- and optical-based platforms to study biomolecular processes at the single-molecule level, with a focus on proteins in native environments. You will take a hands-on role in experimental design, data
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records, and supporting project timelines Lead and manage ethics processes, including preparation of applications, amendments, and ensuring compliance with institutional and ARC requirements Coordinate data
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-throughput synthesis, automation, and data-driven approaches. Key Responsibilities: Design and execute high-throughput synthesis workflows for solid-state materials, including intermetallics, chalcogenides
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investigators. Contribute to study design, data collection, analysis, and interpretation for translational pathology projects (e.g., NGS, IHC, spatial assays, image analysis). Design and perform wet laboratory
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of chronic diseases using wastewater-based epidemiology. This will involve integrating evidence from systematic reviews, epidemiological studies, and wastewater-derived data to build comprehensive, predictive