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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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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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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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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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feasibility, and to facilitate the rapid translation of study findings into registry practice and health data environments. Project goals: The aim of the project is to develop cutting-edge AI 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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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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representations complicate transparency and compliance checks with data protection and privacy legislation (e.g., GDPR) whether performed by humans or computer systems. Second, both privacy-preserving distributed