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) to provide benchmark data as well as the quantum technology gravity gradient sensor. This involves close liaison with colleagues across campus, especially as part of QuSIT, partner institutions, project
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battery data. Develop scalable, secure, and production-grade software systems supporting industrial deployments. Integrate embedded BMS firmware, edge computing devices, and cloud analytics pipelines
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work as part of a larger team to assist with collecting and analyzing data gathered from human subjects, both in field, clinic and lab studies as part of evaluations of the technology. A large part of
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checks required for the role, as determined by the University. Enquiries: Professor, Bernard Flynn, Professor, Chemical Biology and Drug Discovery, +61 3 9903 9650 Position Description: Research Fellow
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team members to ensure all project deliverables are met. Undertake these responsibilities in the project: i. Assists in co-supervision of final year projects (FYP) or capstone projects students under
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design of improved formation and ageing protocols. Role Summary Work within the FAST (Formation and Ageing for Sustainable Battery Technologies) research programme, delivering the data engineering and
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should have: PhD in computer science, software engineering, Embedded Systems, Artificial Intelligence / Machine Learning, data science, or closely related disciplines or significant practical/industrial
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11 Mar 2026 Job Information Organisation/Company Łukasiewicz PORT Department P4Health Research Field Biological sciences » Biological engineering Researcher Profile Recognised Researcher (R2
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under the supervision of Asst Prof Meng Xin from the Department of Civil and Environmental Engineering. This position is part of an exciting research programme aimed at developing generative AI tools
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Carnegie Mellon University is a private, global research university that stands among the world’s most renowned education institutions. With ground-breaking brain science, path-breaking performances