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to efficiently transform biochar into battery-grade hard carbons with controlled characteristics and optimized electrochemical performance. The use of microwave plasma carbonisation will allow
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will be used to validate candidate pathways and biomarkers. Key deliverables include: (i) optimized and benchmarked EV isolation or characterization pipelines; (ii) validated ToF-SIMS/MALDI analytical
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meritorious include: Knowledge of machine learning, reinforcement learning, and optimization, Experience with multi‑modal sensor data (vision, force/torque, proprioception), Experience with simulation
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materials, formulating and optimizing electrolyte systems (including hybrid solvents, functional additives, and water-in-salt electrolytes), and investigating the electrochemical performances of designed
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in clinically relevant environments. Key work assignments include: Design, fabrication, and optimization of high-performance plasmonic nanostructures and SERS substrates for sensing in complex
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results demonstrate that compact heat-exchanger solutions—supported by conceptual design and aerodynamic optimization of integrated ducts—can deliver substantial reductions in specific fuel consumption and
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solutions, followed by parametric optimization and modelling, along with characterization studies. According to the Higher Education Ordinance, a person appointed to a doctoral studentship should primarily
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well as postgraduate and undergraduate education within areas such as autonomous systems, complex networks, data-driven modeling, learning control, optimization, and sensor fusion. The division has extensive
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formation and how local dose is distributed. In the longer perspective, this knowledge will support optimization and translation of bioelectronic implants towards clinical application. In this project, you
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in AI. We expect an excellent publication record in areas such as automated planning, machine learning, logic or combinatorial optimization. Furthermore, candidates should have very good programming