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Antimicrobial resistance (AMR) is one of the most significant and immediate threats to health in Australia and globally. We are working on harnessing new technologies such as artificial intelligence
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of the AAAI Conference on Artificial Intelligence (Vol. 26, No. 1, pp. 267-273). - Blau, T., Bonilla, E. V., Chades, I., & Dezfouli, A. (2022, June). Optimizing sequential experimental design with deep
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intelligence techniques (e.g., Deep Learning, Statistics, ML, Optimization) in order to (1) understand the nature of critical software defects like vulnerabilities; (2) predict; (3) highlight vulnerable code; (4
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the guidance of artificial intelligence techniques. The project will develop novel design processes that embed material behaviour within agent-based and machine learning computational design systems
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working on harnessing new technologies such as next-generation sequencing and artificial intelligence to improve the diagnosis, treatment and prevention of AMR infections. The specific aims of this project
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Project description: On behalf of the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), we will establish the role of artificial intelligence (AI) deep learning to improve the prediction
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expertise in Python and ML platforms such as PyTorch, including a track record of effective deployment of these within artificial intelligence or machine learning projects or client problems. Experience in
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used in commercial software development. A track record of publications in machine learning, computer vision, artificial intelligence and/or robotics conferences and/or journals. Preference will be given
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developing an artificial intelligence-based foundation model tool-box that utilizes a wide range of historical, global and regional weather and climate data. A general purpose tool-box, initially tested
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Artificial Intelligence (GenAI). Position Description Applicants are required to submit a resume and cover letter and respond to a selection criteria questionnaire as part of the online application