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designs and maintains cloud-based ML/AI operations on UNSW’s enterprise data platform. This role operationalises applications by establishing robust MLOps processes and automated pipelines for data
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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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-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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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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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
workflows, and large-scale data analytics. You will design, build, and optimise software environments used daily by thousands of researchers across universities and national research initiatives. Your
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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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University of New South Wales | Canberra, Australian Capital Territory | Australia | about 8 hours ago
that will be undertaken are listed in the Position Description. You will not be required to provide any further documentation or information regarding the checks until directly requested by UNSW. UNSW is
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, data-led HR solutions that enhance performance and employee wellbeing. This is an exciting chance to step into a role where your expertise, judgement, and influence will help build a positive, forward
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scanning tools, and ensuring that threat data is accurately recorded in tracking systems. Key Accountabilities Review scheduled vulnerability scans, vulnerability alerts and threat feeds to identify and flag
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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