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Field
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vision, IoT sensors, and blockchain to monitor food quality, safety and animal welfare in real-time and enhance transparency. AI and machine learning will analyse data from pilot sites to identify
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project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research
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Free probability theory High-dimensional probability, concentration and functional inequalities Mathematical aspects of machine learning and deep neural networks Free Probability aspects of Quantum
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scientific publications, patents, and seeing collaborators translate our work into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and
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), to develop systems that improve the efficacy of machine learning-based technologies for healthcare applications. You must hold a PhD (or be near completion) in a field such as AI, computer science, signal
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considerations. Experience working with machine learning methods for control, perception, or decision-making in physical systems is an advantage. Knowledge of or a passion for sustainable computing
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projects utilizing machine learning, deep learning, and generative AI to solve business and healthcare problems have been undertaken at the Insight Lab. For more details, please refer to: https
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physics. Development of new artificial-intelligence and machine-learning techniques for high-energy and nuclear physics. Close interaction with our collaborators in the EIC and the BNL theory group will be
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. Collaborate on multidisciplinary projects involving high-throughput phenotyping platforms. Apply machine learning and deep learning techniques to improve image processing and trait prediction. Analyze large
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
illnesses. The post holder will also co-supervise a PhD student who will be involved in the same project. This is a highly interdisciplinary project combining forest ecology, remote sensing, machine learning