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universities, and three research and technology organizations. This PhD project is focused on glass cutting, where large, laminated glass panels are cut to sizes for window production. The process involves pre
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and epidemiological characterisation of cardiovascular risk among people living with T1D, using multimodal data in the large SFDT1 cohort study. This work will lay the groundwork for developing novel
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code, combined with advances in automation, analytics and data science, has fundamentally changed the scope and ambition of harnessing the potential of biological systems. Big data approaches and
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team using the contact information below. Note: In the event that no qualified bilingual candidates are identified, NRC may consider unilingual candidates who will be required to commit to achieving
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, including electrical engineering, control theory, industrial engineering, electronics engineering, energy policy, data science, and applied mathematics. As part of the Alliance program, your project will be
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. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will be made by Prof
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links between home and flexible working patterns, mobility patterns and wellbeing. The project will take a multidisciplinary approach combining quantitative analysis of large-scale secondary datasets
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comfortable processing and analyzing large data sets in Python; experience with C++ (or another low-level language) is a plus Strategic thinkers with demonstrated interests in strategic games and/or competitive
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comfortable processing and analyzing large data sets in Python; experience with C++ (or another low-level language) is a plus Strategic thinkers with demonstrated interests in strategic games and/or competitive
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excellent logical reasoning and a passion for turning data into decisions Clear communicators in a fast-paced and highly collaborative environment Programmers comfortable processing and analyzing large data