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of scientific monitoring programs demonstrated experience managing and integrating large datasets experience working in large teams and organising complex field programs a strong commitment to delivering outcomes
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stakeholders develop a coherent research program in translational metagenomics and an emerging research profile support and supervise junior researchers and clinical trainees contribute to the positive culture
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to furthering that agenda contributes to the strategic direction of the Faculty/School/Portfolio as articulated through the Faculty/School/Portfolio’s Strategic Plan/s or equivalent undertake other duties and
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sedimentology, paleoecology, paleoclimatology, numerical modelling, relational database design, and geological data science a strong track record of publications in leading international scientific journals (Q1
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and endothelial dysfunction. The project involves collaborative research with a team of experts in thrombosis, materials science, surface engineering, patient treatment and industry translation. You
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for publication in scientific journals. Present research outcomes at national and international conferences. Collaborate with lab members to contribute to grant proposals and funding applications. Collaboration and
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Frontier for Science Program, and will involve collaboration with researchers from different disciplines across four countries. Your key responsibilities will be to: undertake research in metallomics and
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to: contribute to an interdisciplinary research program investigating the biochemical properties of a novel enzymatic oxygen sensing system and its role in low oxygen diseases facilitate drug discovery efforts
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The School of Medical Sciences is recognised as one of the leading centres for medical science education in the world. With over 100 years of excellence in education, we are proud to be training the next
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-series data interest and experience developing and working with open scientific software interest in analyzing the dynamics of complex physical systems interest in using statistical learning to infer