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: developing and testing new approaches to water resources modelling, application of Bayesian inference methods to environmental problems, machine learning and data science applications, undertaking analysis and
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understanding of non-stationary complex systems through theoretical analysis and numerical simulation develop efficient statistical algorithms for analyzing and inferring dynamical models from multivariate time
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the following key capabilities: A PhD in a relevant field Demonstrated experience working with ecological datasets Demonstrated experience in statistical inference and reporting Knowledge of models and theory in
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-technical audiences and engage in stakeholder or end-user consultation. DESIRED CHARACTERISTICS: Demonstrated experience in models of opinion dynamics, Bayesian reasoning models, natural language processing
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and Ar-Ar geochronology, fission-track and (U-Th-Sm)/He thermochronology, vitrinite reflectance, and thermal history models. New relational data models data for incorporating methods such as include
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collected data from sampling larval, juvenile and adult fish using a variety of methods (seining, BRUVs) and acoustic tracking to measure fish movement and connectivity. Expected outcomes of this project
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approaches (not essential but preferred) Demonstrated ability to undertake high quality academic research and conduct independent research with limited supervision. Demonstrated track record of publications
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successful you will need: A PhD in geology, geochemistry or a related field. An established track record of publication in isotope geochemistry and/or critical mineral ore systems. Demonstrated expertise in
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and Ar-Ar geochronology, fission-track and (U-Th-Sm)/He thermochronology, vitrinite reflectance, and thermal history models. New relational data models data for incorporating methods such as include
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focused on the challenge of accelerating ternary neural networks using FPGA devices. The successful candidate will have significant experience in machine learning, FPGA design and an outstanding track