11 bayesian-inference-tracking Postdoctoral positions at University of Sydney in Australia
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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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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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complex analysis an excellent track record of publishing high-quality papers in top-tier mathematics journals solid mathematical programming skills. Pre-employment checks Your employment is conditional upon
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strong publication record in peer-reviewed journals demonstrated ability to independently design and execute experiments proven track record of generating high-quality, reproducible research data
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or modules a strong track record of modelling, characterising and demonstrating innovative device designs and reflected by publications in high quality journal publications excellent communication skills with
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ternary neural networks using FPGA devices. The successful candidate will have significant experience in machine learning, FPGA design and an outstanding track record in conducting machine learning research
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journals. Disseminating work at national and international conferences. A track-record of interdisciplinary collaboration. Your employment is conditional upon the completion of all role required pre
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sedimentology, paleoecology, paleoclimatology, numerical modelling, relational database design, and geological data science a strong track record of publications in leading international scientific journals (Q1