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Field
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from multiple disciplines and institutions. Required: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong background in machine learning / computer
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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 months ago
intelligence (AI), machine learning (ML) and vision, natural language understanding, and robotics, to build autonomous systems that can perceive, plan, and respond to their environment in pursuit of high-level
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, science and the economy. The postholder will be responsible for undertaking research in generative AI and machine learning methods for audio generation and audio-related multimodal content generation
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to assess potato dormancy break, including: data collection, processing, AI model development and classification accuracy assessment. Involved in supporting an electrophysiology-based machine learning model
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and the economy. The postholder will be responsible for undertaking research in generative AI and machine learning methods for audio generation and audio-related multimodal content generation, including
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The fellow will be responsible for: Building collaborations with our multidisciplinary team (medical physicists, engineers, computer scientists, nuclear medicine physicians) to develop and implement innovative
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of Physics, Mathematics, and Computing (PMC) at UWA. The PMC School has a strong tradition of world-leading research in computer vision, mathematical physics, data science and quantum measurement. About the
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for (Selection Criteria): Essential: A doctoral qualification in a relevant engineering, computing, or physical sciences discipline. Demonstrated research expertise in data analytics, machine learning, artificial
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scientists, nuclear medicine physicians) to develop and implement innovative AI algorithms applied to medical images To lead effort on enabling translational and physician-in-the-loop AI solutions for medical
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modeling. These projects will focus on how to leverage ideas from a combination of partial differential equation based computational methodologies and machine learning techniques to develop computational