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Field
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candidate will support research and development projects that advance the state of the art in machining science, machine tool design and characterization, manufacturing process optimization, and
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to additive manufacturing (AM), virtual manufacturing, material characterization, topology optimization, and real-time sensing. This position resides in the Computational Sciences and Engineering
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Automatic Control group (from Department of Electrical Engineering) and the Mathematical Optimization group (from Department of Mathematical Sciences). About the research project The project focuses on
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also explore machine learning and artificial intelligence-driven approaches to industry challenges, such as warehouse management, ocean freight optimization, and reshoring high-technology industry
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that learn constitutive parameters from a single informative test through Bayesian optimization, together with quantification of measurement and model-form uncertainty. Approximately half of the effort
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also explore machine learning and artificial intelligence-driven approaches to industry challenges, such as warehouse management, ocean freight optimization, and reshoring high-technology industry
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, and process optimization. You enjoy hands‑on laboratory work, iterative troubleshooting, and mentoring junior researchers. You are collaborative, organised, and committed to producing high‑quality
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photovoltaic systems, biomass-based combined heat and power (CHP), and application-specific energy storage systems. We are also exploring how quantum computing can unlock new optimization and control
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-quality research on the intersection of machine learning, control, optimization and/or power system dynamics, both from a theoretical/analytical point of view and in terms of coding. We are looking for
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performance utilizing a variety of tools including life-cycle assessment (LCA), machine learning techniques, and/or optimization methods. This position will carry out state of the art research into the