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algorithms suitable for multi-static and distributed geometries. Understanding the performance limits of such systems, including sensitivity to synchronisation errors, geometry, transmit time, and partial
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network integration for emerging low-energy opto-electronic AI systems and beyond. The challenge: Machine learning and neural networks are super-charging the complexity of problems that computer algorithms
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this project, you will develop the next generation of federated machine unlearning algorithms—methods that can efficiently deliver genuine, verifiable, and robust erasure without sacrificing model performance
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of algorithmic systems. The research will investigate how clinicians interact with automated and machine learning–based decision-support systems, with a particular focus on cognitive workload, trust, situational
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of the screening output. This approach selects new, efficient enzymes, but also generates unique sequencefunction datasets that will be interpreted by regression and tree-based machine learning algorithms to obtain
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with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains from
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functional theory. In collaboration with Phasecraft, a leading quantum algorithms company, this project will explore the generation of new quantum computing datasets and the development of machine learning
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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sustainability. The research will delve into power-aware computing strategies, thermal management, and the development of algorithms that balance performance with energy consumption. Students will aim to create
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-critical systems. The research will focus on developing AI-powered verification tools, health monitoring algorithms, and compliance assurance techniques that ensure system reliability throughout