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Neuro-symbolic AI combines the strengths of neural and symbolic methods to efficiently learn and reason over models of the world. Typically, many of the assurances that can be provided by such systems are limited to the learning errors due to the neural component. In this Ph.D. project, you will...
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Neuro-symbolic AI combines the strengths of neural and symbolic methods to efficiently learn and reason over models of the world. Typically, many of the assurances that can be provided by such systems are limited to the learning errors due to the neural component. In this Ph.D. project, you will...
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collaborating with industry partners on a project aimed at developing kinetic Monte Carlo simulations to model epitaxial growth processes. The goal is to control and optimise the growth of nanoscale structures
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strengths in Bayesian and Monte Carlo Methods, Biomathematics, Biostatistics and Ecology, Computational Mathematics, Data Science, Dynamical Systems and Integrability, Finance and Risk Analysis, Mathematical
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research strengths in Bayesian and Monte Carlo Methods, Biostatistics and Ecology, Combinatorics, Data Science, Finance and Risk Analysis, Nonparametric Statistics, Optimisation, Stochastic Analysis, and
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Physics and Number Theory. More broadly, the School also has research strengths in Bayesian and Monte Carlo Methods, Biomathematics, Biostatistics and Ecology, Computational Mathematics, Data Science
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candidate will have demonstrated expertise in computational methods in statistics, particularly in simulation-based inference methods, or Monte Carlo methods, and their associated theory or applications
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interests in the areas of applied and pure mathematics, and statistics. In Statistics, the School has research strengths in Bayesian and Monte Carlo Methods, Biostatistics and Ecology, Combinatorics, Data
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University, Perth, WA, Australia with Dr. Navdeep K Dhami in her ARC Discovery project in collaboration with Prof. Allan Pring (University of Adelaide), Dr Carlos Rodriguez (University of Granada, Spain) and
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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning and deep learning algorithms for dynamic systems (sequential or time-series data). Experience