40 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at University of Sydney
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to these values. We are seeking an excellent candidate for a Postdoctoral Research Associate in 3D Deep Learning for Vision and Lidar position who has: a PhD (or near completion) in a relevant field background
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celebrated. We are continuously working to identify and remove biases and barriers in an effort to make our workplace open, supportive and safe for everyone. To learn more about the School of Physics, click
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Acceleration who has: strong experience in FPGA design, machine learning or a related field in the case of the Postdoctoral Research Associate, a PhD (or near completion) in FPGA design, machine learning or a
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density) influence energy dissipation develop mathematical models to predict and explain these effects collect and analyse data, including with the use of machine learning use this knowledge to design
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draws on large-scale data collection and archival records, and applies a diverse set of methodologies such as text analysis and machine learning to assess the impact of colonisation. About you PhD in
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. Our mission is to develop life-changing treatments for glaucoma and other optic nerve diseases that cause irreversible blindness. Learn more about the Snow Vision Accelerator: https
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-Driven Sensing and Control for Megaproject Systems who has: a PhD or near-completed PhD in one of the following fields (or closely related): project Management or Project Studies (with strong computational
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that contributes to a competitively funded research program in neuroimmunology and chronic pain undertake recruitment of chronic pain patients for clinical research studies undertake wet laboratory research
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computer literacy and proficiency in using various research and administrative software applications, that may include Microsoft Suite, REDCap, SPSS and other relevant data analytic software excellent
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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