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PhD Position - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based elaboration of biocatalysis for synthesis. A doctoral position is
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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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candidate will develop and conduct empirical research examining human interaction with algorithmic decision-support systems in clinical contexts. The precise focus of the project will be refined
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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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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
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Technology Centre (MTC). You will push the limits of multiphysics CFD for laser manufacturing by developing a next-generation simulation capability for laser drilling - with relevance to additive
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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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on developing the simulation models, data models and algorithms required to enable connected cross-disciplinary design and optimisation, laying the foundations for more integrated and intelligent engineering