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) in FPGA design, machine learning or a related field experience in the development of machine learning models using Python and pytorch expertise in two or more of the following technical areas: design
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related field experience in the development of machine learning models using Python and pytorch expertise in two or more of the following technical areas: design of FPGA-based accelerators, high-level
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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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-based algorithms (e.g., GNNs, deep reinforcement learning) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute
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learning at scale. Research directions include designing algorithms and methods for adaptive and personalised feedback, modelling learning behaviours with sequence and deep learning methods, and generating
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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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: 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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models to enhance the detection of online narratives. The successful candidate will be appointed to a two-year fixed-term, research-intensive position supported by ASCA’s EDT program on synthetic media and
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disciplines including aerospace, combustion, design, fluid mechanics, materials, mechanical, mechatronic and robotics engineering. To learn more about the School click here . The Clean Combustion Group