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integrating GIS data, IoT sensor measurements, and domain-specific information. AI models will be developed to predict dynamic behaviors and key risk zones, and these models will be embedded into the digital
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integrating GIS data, IoT sensor measurements, and domain-specific information. AI models will be developed to predict dynamic behaviors and key risk zones, and these models will be embedded into the digital
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Australian National University | Canberra, Australian Capital Territory | Australia | about 20 hours ago
Nations and tools and processes for water resource management. Supervisor: Dr Serena Hamilton, Fenner School of Environment & Society Optimal Cross-Scale Irrigation Operation in the Era of Sensor
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Primary supervisor Zhaolin Chen Research area Data Science and Artificial Intelligence Machine learning has recently made significant progress for medical imaging applications including image
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representational form of the moving image for the past 150 years, VR introduces a new paradigm for cinematic expression and viewing experience. This challenge marks a transformational moment in the
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bioelectronic medicine. The project builds on the lab's track record in flexible sensor arrays, field-effect-transistor biosensors, and functional nanomaterial interfaces, and will contribute to
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image, dermascopic image, MRI, CT-scans and EHR, form the basic screening and diagnosis procedure for many diseases of interest. The goal of this project is to develop computational models using text and
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. Required knowledge Python programming Machine learning background Text analysis Image analysis Learn more about minimum entry requirements .
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for detecting and mitigating the presence of deepfakes in digital content. Required knowledge Python programming Machine learning background Image analysis Video analysis Audio analysis Learn more about minimum entry requirements .
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tumour. We will use various Deep Learning techniques, such as CNN, and will experiment with a variety of Deep Learning frameworks, such as U-Net, ResNet, etc. One of the main challenges in medical image