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and unsupervised machine learning models, including LLM-based classification and fine-tuning for domain-specific applications. Collaborate with faculty and research staff on data collection and analysis
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and intelligent systems for industrial applications. Based within the School of Engineering and Architecture, IERG develops and applies cutting-edge analytics, modelling, and decision-support
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. quantitative and/or qualitative counterfactual-based approaches, Difference in Difference models, Qualitative Comparative Analysis, Bayesian hierarchical modelling); o Experience working with and synthesizing
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platforms, enabling scenario analysis, monitoring, and traceability. Support aggregation and scaling of organisational-level results to inform national-level modelling and evidence-based policymaking
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wave and flood modelling, IoT and intelligent control systems, renewable energies, robotics, automation, and supply chain management with academics and students working together on projects that address
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Python, PyTorch, and Linux/command line Familiarity with LLM in-context learning and prompt engineering Basic understanding of modern LLM models, ecosystems, and pipelines, including retrieval-augmented
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at a pilot facility. The candidate will develop a simplified, control-oriented dynamic process model. This model will be used to propose control structures and analyze them. An available complex steady
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). Contribute to sport business and operations analytics projects as needed (e.g., ticketing, fan engagement, and scheduling). Build and maintain data pipelines and models using R, Python, SQL, and cloud
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Center policies and procedures for infection control, safety, and administrative and clinical practice. Embraces and consistently role models the UCSF concepts of values: PRIDE (Professionalism, Respect
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. The candidate will study architectural models, including the placement and roles of quantum repeaters, memories, and control-plane functions, and how these integrate with classical networking and orchestration