262 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Dr" positions at Monash University in Australia
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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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-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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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
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multiple teaching periods and campuses. This position will be responsible for assisting in the preparation and maintenance of assessment timetables, ensuring accurate data entry and calendar management
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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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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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energy, water, food, and waste. You will play a key role in the project’s conceptual development, undertake qualitative data collection and analysis, and co-author publications that support environmental
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Minimum Message Length (MML) is an elegant information-theoretic framework for statistical inference and model selection developed by Chris Wallace and colleagues. The fundamental insight of MML is