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Integrated Planning & Learning and Reinforcement Learning in non-deterministic and partially-observed scenarios. The methods will be evaluated on physical robots. The ideal candidate would have: A PhD (or
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; ongoing learning and development opportunities to grow your career; an inclusive and supportive culture and environment to work in, both online and on campus. Who are we? Deakin is a cutting-edge public
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. Desirable: Proficiency in scientific programming (e.g. Python) and familiarity with data science and machine learning techniques. Experience with geochemical analytical techniques and working in a laboratory
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experience contribute to ongoing translational research program related to the application of statistical and machine learning methods in reproductive and perinatal medicine using both clinical quality
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peer-reviewed publications, while providing senior-level expertise to support and mentor internal teams. A vital responsibility includes guiding emerging science-practitioners (Clinical Psychology PhD
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simulations using DFT (particularly of surface processes); kinetic Monte Carlo simulations; molecular dynamics simulations; classical and machine-learned force fields. Highly developed skills in scientific
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), clinical trials, disease surveillance, and the use of novel methods including Bayesian network, machine learning, social network analysis and dynamic data visualisation tools. Further information is
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-atomic potentials using a combination of classical and machine-learning (ML) approaches (and a new hybrid method recently developed in our group). Some of the types of simulations that will be performed
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. Desirable: Proficiency in scientific programming (e.g. Python) and familiarity with data science and machine learning techniques. Experience with geochemical analytical techniques and working in a laboratory
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and standing recognised by the University/profession as appropriate for the relevant discipline area (e.g., AI/Machine Learning, Bioinformatics). A proven track record of research and scholarly