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ANSTO (Australia), Imperial College London (UK), and Tokamak Energy (UK), is launching a groundbreaking research program focused on developing new materials for compact tokamak reactors. We are recruiting
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group of A/Prof Patrick Burr, please visit https://www.patrickburr.com/group In your role you will be responsible for performing ab-initio and molecular dynamics simulations, and for developing inter
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of Natural Sciences at Macquarie University. We are seeking a suitable qualified postdoctoral research fellow to join a team with an international reputation for producing high quality research outcomes in
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. Experience using high-performance computing resources and automated data pipelines. Strong publication record and ability to communicate scientific results effectively. Capacity for collaborative work in
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or metabolomics. Experience using ‘R’ and high-performance computing preferred. Evidence of publications in reputed refereed journals and presenting at conferences. Evidence of contributions towards successfully
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the timing, scale, and rate of mammal declines in Australia. They will use critical inferences of past demographic change and high-performance computing to disentangle the ecological mechanisms that were
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Philosophy of Life Sciences (Philosophy of Biology or Cognitive Sciences) relevant to the research project A strong track record of publications in high-quality peer reviewed journals in Philosophy of Biology
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in Philosophy of Life Sciences (Philosophy of Biology or Cognitive Sciences) relevant to the research project A strong track record of publications in high-quality peer reviewed journals in Philosophy
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responders and operational risk assessment regarding skin decontamination and will build on a program of work focused on dermal exposure to chemicals. To be successful you will need: Completion of a PhD in
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transformative sensing technologies. engineer novel fluorescent probes through advanced design, synthesis, purification, and characterisation techniques. perform high-resolution fluorescence microscopy experiments