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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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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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of recruiting talent aligned to these values. We are looking for a Postdoctoral Research Fellow who has: a PhD (or in near completion) in the relevant fields of Artificial Intelligence, Machine Learning, HCI
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): project Management or Project Studies (with strong computational/AI expertise) systems Engineering or Control Systems (with applications to large-scale projects) artificial Intelligence / Machine Learning
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/AI expertise) systems Engineering or Control Systems (with applications to large-scale projects) artificial Intelligence / Machine Learning (with interest in applications to megaprojects or governance
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Postdoctoral Research Associate in Global Environment Modelling of Soil Organic and Inorganic Carbon
. The project is aimed to improve our in-house developed process-based computer model and use it to represent the soil ecohydrological and biogeochemical interactions across various carbon and nitrogen soil pools
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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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to help monitor and combat online social influence and promote a healthy information environment. The successful candidate will join the School of Computing and Mathematical Sciences as part of a project
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) Country Australia Application Deadline 27 Sep 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is
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of scientific monitoring programs demonstrated experience managing and integrating large datasets experience working in large teams and organising complex field programs a strong commitment to delivering outcomes