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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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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
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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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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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, paleoecology, paleoclimatology, numerical modelling, relational database design, and geological data science a strong track record of publications in leading international scientific journals (Q1) and effective
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expertise in at least one of the following: geochemistry, carbonate sedimentology, paleoecology, paleoclimatology, numerical modelling, relational database design, and geological data science a strong track
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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