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international leadership and innovation, focused on developing new methods, whether experimental, analytical or computational. Our research spans the range from solid earth evolution to climate processes, and
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for (Selection Criteria): Essential: A doctoral qualification in a relevant engineering, computing, or physical sciences discipline. Demonstrated research expertise in data analytics, machine learning, artificial
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their academic pursuits. Key responsibilities will include: Research: Establish a research program in MND, collaborate on research projects, seek and manage research funding, publish in reputable journals, utilise
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Attractive options for salary packaging Fitness Passport Employee Assistance Program Professional development support Well-equipped campus facilities including medical, cafes, gym and childcare. What matters
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on applied, industry-relevant quantum research in areas such as: Quantum sensing, navigation, and timing Quantum materials and manufacturing Quantum computing and communication Quantum fundamentals RAQT serves
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, industry-relevant quantum research in areas such as: Quantum sensing, navigation, and timing Quantum materials and manufacturing Quantum computing and communication Quantum fundamentals RAQT serves as a
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Professor – Computational Biology E: martin.smith@unsw.edu.au Contact (process related questions) Anabel Wason - Talent Acquisition Consultant E: a.wason@unsw.edu.au Applications close: Sunday 8th June 2025
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suicide prevention or in domains strongly linked to increased risk of suicide and suicidal behaviours. The research program is expected to result in high quality outcomes that meaningfully contribute to new
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Trudinger and Po-Lam Yung. The primary aim of the positions is to carry out the research programme proposed for the Discovery projects. This will be done in collaboration with the two investigators above and
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projects on time and within budget. Expertise in quantitative genetics, genomics, breeding program simulations, or related areas, preferably in grain crops, with a focus on modelling genetic-environment