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Field
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, including calibrating hardware and software infrastructure for research, sensor data collection and analysis; validating and disseminating research protocols and tools; creating technology prototypes and
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experience in data-driven modelling and a strong interest in large language models (LLMs) and their application to scientific and engineering workflows. Solid foundations in mathematics, engineering, or a
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(including Large Language Models and data-driven optimisation) with concrete industrial deployment. A profile comfortable with the challenges faced by industries in their transition towards Manufacturing 4.0
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-informed / simulation-aware modeling Efficient algorithms for design-space exploration (e.g., surrogate modeling, Bayesian optimization, differentiable programming) Hybrid approaches combining data-driven