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Field
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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
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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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of the research project(s), including design, fabrication, characterization, and modeling of metamaterial fibers and textiles. Develop and optimize fabrication processes for fibers and textiles. Conduct optical
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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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have an interest in detecting and optimizing spray deposition on plants and reduce environmental risk of plant protection products by reducing exposure of soil-water and bystanders? Are you interested
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of three interlinked sub-projects: 1. Time-Series Feature Extraction 2. Deep Learning for Price Forecasting 3. Execution Timing and Portfolio Optimization You will: ● Conduct independent research under
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electromagnetic simulation techniques and incorporate the characteristics of new materials to design motor structures, and perform multi-objective optimization aimed at enhancing motor efficiency, power density
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electrolyte and electrodes and their influence on the overall performance and safety of sodium-based batteries and their chemistry You cooperate with partners from theory to optimize charge transport
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, enabling multi-vessel charging and scalable maritime electrification. Advanced control strategies and data-driven optimization methods will be considered to enhance the efficiency, sustainability, and