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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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broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data
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approximation algorithms for deriving dual bound within a branch-and-bound algorithms. Other directions could use Machine Learning or new decompositions. This subject is generally quite open so it is important to
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/C++) computer codes implementing a cryptographic algorithm. Although desired, background in advanced cryptography is not a must. Application of a PET algorithm to solve a real-life problem: This
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science conference [1]; one of our papers is recognised as Clarivate Web of Science HighCite (top 1% of papers for the field of research) [2]; three of our algorithms (TS-Chief, InceptionTime and Rocket
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as networks or graphs in the hope of reasoning about them - but the tools that we have for understanding such network structured data (whether algorithmic analytics or visualisation tools) remain crude
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Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available
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This project will investigate and develop the ways in which AI algorithms and practices can be made transparent and explainable for use in law enforcement and judicial applications The Faculty
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Connected Autonomous Vehicle (MCAV) team. Required knowledge Artificial Intelligence Machine learning Software Testing Genetic Algorithms
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challenging data problem. Weak signals from collisions of compact objects can be dug out of noisy time series because we understand what the signal should look like, and can therefore use simple algorithms