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Field
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become the bottleneck in achieving optimal performance and trustworthiness. This project will focus on how a federated multi-task learning framework can be effectively designed and optimised to address
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will focus on building understanding of the interplay between the upstream process parameters and process performance to develop approaches and strategies to optimize efficiency and robustness and allow
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networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
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its environment and respond optimally in dynamic operating conditions. Meanwhile, you will also develop intelligent control strategies that minimise energy use while ensuring punctuality and safety
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2:1 undergraduate honours degree in a relevant subject and meet our English language requirements. They should have a strong background in physics and/or mathematics (e.g., PDE, optimization) and/or
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to simulate sewer networks as dynamic systems, targeting ≥90% modelling accuracy. Train an explainable decision-making agent to optimize interventions (e.g., pipe upgrades), balancing cost, equity, and
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main focus on the development of control software. ▪ You will design and implement advanced control and readout protocols and optimize experimental characterization workflow,s leveraging machine learning
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system analysis or comparable Initial experience in optimization or statistics is an advantage Initial experience of an object-oriented programming language (e.g. Python, Matlab) and MS Office Analytical
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the theory of optimization algorithms and high-dimensional statistics to address some of the most fundamental questions in ML such as the behavior of neural networks. The environment of this project is highly
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on designing and synthesizing advanced materials for next-generation batteries—such as solid-state, multivalent-ion, or aqueous rechargeable systems—while collaborating with the research team to optimize