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financial, personal, and confidential information. This project seeks to introduce machine learning and artificial intelligence techniques to effectively detect phishing websites. By leveraging these advanced
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This research project aims to address the critical need for privacy-enhancing techniques in machine learning (ML) applications, particularly in scenarios involving sensitive or confidential data
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based on matched-filter statistics. Detecting the unknown relies on the development of complex algorithms at the forefront of statistics, machine learning, and data science. This multi-disciplinary
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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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strategies using hybrid quantum-classical approaches. While current learning analytics systems rely on classical statistical and machine learning techniques, they often struggle to capture the complex
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This project aims to employ advanced machine learning techniques to analyse text, audio, images, and videos for signs of harmful behaviour. Natural language processing algorithms are utilized
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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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networks that can be trained to do machine learning and AI tasks in a similar way to artificial neural networks. In this project you will develop machine learning theory that is consistent with the learning
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We are seeking a motivated PhD candidate to work on unsupervised music emotion tagging within the broader field of affective computing. The project aims to develop reproducible machine learning