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Field
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foundational and advanced Artificial Intelligence topics such as Python programming, data analysis, machine learning, artificial intelligence tools and frameworks, neural networks, and ethical considerations in
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neural networks) to design and optimise integrated photonic devices and metasurfaces. This includes building automated workflows that link electromagnetic simulation tools with AI models to accelerate
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-binding and deep-learning neural networks. These methods will be integrated into the next-generation Amber software suite used worldwide. The project is to design and implement new high-performance software
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experience involving data pre-processing and preparation for machine learning models Demonstrable research experience in conducting experiments for training and evaluating deep neural networks Knowledge
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recognition, brain-inspired or spiking neural network approaches, predicting material properties, optimizing processing parameters for next-generation energy technologies, analysis of "big data" generated from
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neural network tool to design multilayered compound metasurfaces for multifunctional operation. • Collaborate with nanofabrication teams to prototype and validate designs. • Conduct optical
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particularly interested in candidates with expertise in data science and AI tools (for example, applied robotics, machine learning, predictive analytics, large language models, and neural networks) that can be
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pipelines, and LLM‑integrated systems. Strong background in network science and graph analytics, including: Graph modeling and analysis using tools such as NetworkX Graph‑based ML or graph neural networks
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, applied robotics, machine learning, predictive analytics, large language models, and neural networks) that can be applied to one or more of the following areas: digital archaeology, remote sensing
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data (a deep-learning (DL) convolutional neural network (CNN) tool trained on ultrasonic B-/C-scan data, unlocking automated objectivity in characterising similar defects in other composite components