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Australian National University | Canberra, Australian Capital Territory | Australia | about 6 hours ago
university Advance transdisciplinary scholarship through research, collaboration and high-impact practice Engage with regional communities in the development of community-led flood intelligence networks
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 2 hours ago
high-impact practice Engage with regional communities in the development of community-led flood intelligence networks Position overview This fixed-term position will be based at the School of Art and
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Medical School Seeking a highly skilled project manager with experience in complex, multi-stakeholder research initiatives. Part-time (0.5 FTE) appointment on a fixed term basis for 12 months. Base
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) adaptation and scaling of the model of care, to support national uptake and ongoing evaluation. This role requires close collaboration with an international network of researchers, policymakers, and service
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an employer contribution of 17% superannuation. 24 Months Fixed-term contract | Part-time or Full-time opportunity. The Australian Plant Phenomics Network (APPN) was established in 2009 under the Australian
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Indigenous staff can shine. Our Indigenous Employee Network meets monthly to cultivate community and connection. We also hold regular yarning circles. We understand the importance of cultural and ceremonial
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until June 2027 • Location: Macquarie University (Wallumattagal Campus, North Ryde) Join a leading ecology and evolutionary biology research team to uncover the role of ecological interaction networks as
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Mechanical systems and processes. The School works across all scales, from nanoscale to personal devices to industrial complexes – building pilot plants to put our research into action. The School builds
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Complex Systems. Applicants are invited to propose a research project around understanding the impact of regenerative design interventions in complex industrial systems, particularly food systems
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. The objective of this project is to explore how Physics-Informed Neural Networks (PINNs) could enhance the detection, classification, and interpretation of weak and noisy signals in complex environments