260 data-"https:"-"https:"-"https:"-"https:"-"Dr"-"UCL"-"UCL" positions at Monash University
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motif, hence renders the identification of the binding protein difficult. Here we propose for the first time to apply the Bayesian information-theoretic Minimum Message Length (MML) principle to optimise
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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
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back at least as far as 1954 (Dowe, 2008a, sec. 1, pp549-550). Discussion of how to do this using the Bayesian information-theoretic minimum message length (MML) approach (Wallace and Boulton, 1968
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-disciplinary team of clinician scientists and computer scientists to develop diagnosis/predictive/treatment/robotics surgery models of diseases of interest using multimodal medical data, consisting of images
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of the U.S. workforce. We then consider various attributes of these occupations, as given by the Occupational Information Network (O*NET) data-base. Using a subset of these occupations, we survey a
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extract events and mine knowledge from existing unstructured/structured data, and exploit the knowledge via neuro-symbolic reasoning for crime prevention (eg -sexual assaults), especially when there is no
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) to support surgeons, operating room technicians, and other professionals in and around operating room activities. Particular areas that may be explored are: Immersive OR analytics: using XR to analyse data
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innovative, data-informed strategies to optimise demand across both Undergraduate and Full-Fee Postgraduate segments, ensuring enrolment and load targets are achieved. Working within the broader Domestic
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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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of the Australian Higher Education System,” which aims to analyse the Australian tertiary system using linked ABS and Department of Education data to inform policy on graduate earnings, peer effects, and field