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will design quantum-safe threshold encryption and/or authentication algorithms. The expected outcome is the design of methods, techniques and their software prototype to implement quantum-safe threshold
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older adults. The expected outcome is the creation of AI algorithms to detect early signs of neurodegenerative disorders in older adults living independently at home. The potential benefit is early
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the development of new algorithms for processing, analysis and inversion of active and passive seismic data and the application of these algorithms to field data. Student type Future Students Faculties and centres
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accelerate the global shift to electric mobility. This PhD will build an AI-driven fleet-scheduling framework that learns from battery data in real time and optimises charging, operations and maintenance
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software frameworks, algorithms, robust testing and validation methods, and/or empirically validated solutions that contribute directly to social good, promoting trust, fairness, transparency, and
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algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have
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and polyploid crop species and benchmark them against other methods such as graph-based methods. This project will combine algorithm development and computational programming with large population
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start up environment. This is an unpaid role with flexible scheduling to complement your studies. The experience will provide exposure to both academic research and the fast moving world of biotech
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to forecast disease burden under alternative vaccination schedules and to assess the impacts of completed vaccination programs against baseline transmission counterfactuals. This project will build on existing