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. Essential qualifications and experience a PhD (or near completion) in one of the following fields (or a closely related discipline): Computer Science, Artificial Intelligence, or Machine Learning Economics or
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, or simulation-based models to study how decisions, incentives, feedback, and learning evolve over time in megaproject environments investigate system-level mechanisms, including stability, path dependence, lock
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, measure transport and machine learning on developing a novel mathematical framework for identifying reduced dynamical models of high-dimensional complex multi-scale systems. The project will develop fast
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environment with a team of a dozen academics, several postdoctoral researchers, and around 20 PhD students. You will benefit from their collective expertise in physics, geomechanics, geotechnical engineering
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make our workplace open, supportive and safe for everyone. To learn more about the School of Physics, click here About you The University values courage and creativity; openness and engagement; inclusion
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of Post-Doctoral Fellow will report to Associate Professor Anthony Newall and has no direct reports. Some key skills required: A PhD (or equivalent experience) in a quantitative discipline, featuring
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. To learn more about the School of Physics, click here About you The University values courage and creativity; openness and engagement; inclusion and diversity; and respect and integrity. As such, we see the
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and the award of over $7 million in research funding each year. Our staff are also recognised for their outstanding commitment to teaching excellence. Click here to learn more about School of Chemistry
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. Our staff are also recognised for their outstanding commitment to teaching excellence. Click here to learn more about School of Chemistry . This position is lab‑based and requires onsite work at the
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strong sense of community & inclusion Enjoy a career that makes a difference by collaborating & learning from the best At UNSW, we pride ourselves on being a workplace where the best people come to do