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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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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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image diagnosis. The expected outcomes are development of a software prototype, technical advancement in medical image diagnosis and the creation of novel AI algorithms. Potential project benefits
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Learning, Algorithms and Data Structures
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Background in Machine Learning, Algorithms and Data Structures
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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
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Project description: Nowadays, data-driven machine learning algorithms are well suited to solve real-world problems that require high-level prediction accuracy. However, it seems as if nothing beats
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The existing deep learning based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them on brain EEG time series (65
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will provide new models and algorithms for energy-transport integration, advancing the knowledge of mitigation strategies for sustainable urban development. #sustainability PhD student role description
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distributions. We wish to represent the biological networks into proper formats, e.g., vector representations, so that existing machine learning algorithms (e.g., support vector machines) can readily be used