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expected to cover multiple cities in multiple countries in the majority world. The longitudinal approach covering multiple decades likely calls for the digital processing of archival, newspaper and policy
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to significantly reduce the turnaround time of SRS, thus enabling their use for industrial design processes. By combining state-of-the-art numerical methods and data-driven modelling techniques, the PhD candidate
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, this PhD will explore machine-learning (ML) methods to significantly reduce the turnaround time of SRS, thus enabling their use for industrial design processes. By combining state-of-the-art numerical
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vernacular knowledge? Then join us in advancing digital fabrication for a post-carbon built environment! Information This fully funded PhD position invites applications from candidates interested in
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our
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, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in
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Engineering with excellent grades. Knowledge of Digital/Mixed-Signal Integrated Circuit (IC) design and Computer Architecture. Solid skills in HDL (Verilog, VHDL) and scripting languages (Python, TCL
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Corrosion Technology? Join us on our journey to ensure Safe Operation of Military Systems. Job description Join the Corrosion Technology and Electrochemistry (CTE) group, part of the Department of Materials
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. Technology, work, and the future of organizing—investigating how digital technologies, platforms, and AI transform work, occupations, and organizing processes Professions, occupations, and expertise