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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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-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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nutritional data into a user-friendly platform, enabling consumers, restaurants, and policymakers to make informed food choices and reduce diet-related emissions. Required knowledge Data analytics and software
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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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Build software that supports CSIRO’s leading research Work in an agile, innovative, and autonomous team Collaborate with experts across tech and science The Data Management and Applications (DMA
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on clinical, genomics and functional dependency data (CRISPR, drug screens). Brain tumours represent the second most common cancer and the most common solid tumour in childhood in general. Paediatric brain
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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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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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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 24 days 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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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