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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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Background in Machine Learning, Algorithms and Data Structures
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substantial research project, GPA 80%+ from a reputed university Refereed publications including journal or conference of high repute Desirable Background in Algorithms and Data Structures
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, from swarm robotics to mesh networks. The prototypical model system for the investigation of self-organised task allocation are social insect colonies, such as bees and ants. They are able to distribute
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designing and implementing new algorithms to produce visual aids to assist people to reason with causal Bayesian networks, as well as the planning and conduct of exploratory usability studies to assess
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Species’ distributions are shifting in response to global climate change and other human pressures. Accurate methods to monitor and predict distribution shifts are urgently needed to manage
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and algorithmic bias) towards the underprivileged students. In the first line of research, we will focus on students whose disengagement with learning can be explained by their poor financial conditions
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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
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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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at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need