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) national consultation and co-design of a model of transitional healthcare for people leaving prisons; (2) a multi-site randomised controlled trial (RCT) of the model of care, in south-east Queensland; and (3
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Biomedical Engineering, Nanotechnology, or a related discipline. Strong hands-on experience in microfluidics (design, modelling, and experimental implementation) Knowledge of nanomaterials and their use in
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and model accountability, connectedness, innovation and care. To be considered for this position, you will have; Education: A bachelor’s degree in health sciences, with evidence of further study
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. Working closely with the ABI program’s leadership team, you’ll contribute to data curation, protocol development, predictive modelling, and novel analytics for large-scale neurocritical care datasets. As
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-atomic potentials (e.g. pro-fit, MLIP-3). Knowledge of meso-scale models such as cluster dynamics (e.g. Xolotl, Centipede), object-kinetic Monte Carlo or similar. Proven commitment to proactively keeping
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out important research with the goal of enhancing knowledge, clinical practices, and care models, focusing on: • Research to enhance child health, well-being, and development • Building equitable health
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essential, mathematical theory frequently suggests its value is low. This misalignment creates a gap between modelling and practice. The research will focus on developing new theory to explain why
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out important research with the goal of enhancing knowledge, clinical practices, and care models, focusing on: Research to enhance child health, well-being, and development Building equitable health
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Job Reference: 1005770 Your new role You will lead modelling work on the containment and eradication of a prominent pest species, Polyphagous Shothole Borer (PSHB) in Western Australia. The work
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 29 days ago
deep learning theory and practice. Applicants with expertise in probabilistic modelling, approximate inference, deep learning, or Bayesian optimisation are encouraged to apply. Interpretable Machine