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Field
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opportunity to field test and validate their methods using real-world systems. Postdoctoral fellows will work across the following research areas: Predictive machine learning Robust and stochastic optimization
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health monitoring devices. This role will involve exploring new materials, technologies, and fabrication techniques to enhance the performance, flexibility, and scalability of health monitoring systems
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the performance of RF sensors in increasingly challenging operational environments. This work spans active radar, passive radar, and electronic support measures (ESM) systems, which were traditionally developed as
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Institute for Climate and Sustainability as part of a broader research initiative on enabling the transition to a deeply decarbonized and reliable energy system. The Postdoctoral Fellow will work closely with
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implementing ultra-low flux melt refining for gas-fired, reverberatory, and other industrial aluminium furnaces. The work includes designing process recipes with flow control, thermal management, and alkali
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for the development of Performing literature review and background study on multi-modal AI safety Apply knowledge on large multi-modal models to optimize training and inference processes Conduct experiments to compare
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and improve the robustness of the analysis, optimization, modelling and inverse design of microwave components and systems. Key Responsibilities: Conduct independent and collaborative research on AI
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conversion layers. The successful candidate will develop robust PQD/polymer composite films, optimize scalable film fabrication processes, and perform comprehensive optical and structural characterization. Key
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the mentorship of leading experts in one of the following priority research areas: Research area 1: Intelligent Structural Optimization using Physics-Informed Reinforcement Learning Research area 2: AI-Enhanced
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: Data Curation: Collect and organize datasets along with relevant clinical information to support and drive research studies Radiomics Pipeline Development: Design and optimize robust radiomics pipelines