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Skip to main content. Profile Sign Out View More Jobs PhD scholarship in Data Science Applications using Multi-Scale Models for Sustainable Bioproduction - DTU Biosustain Kgs. Lyngby, Denmark Job
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Skip to main content. Profile Sign Out View More Jobs PhD scholarship in Behavioural Experiments with Tradable Credits for Mobility – The Green Token Project – DTU Management Kgs. Lyngby, Denmark
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developing multiple privacy preserving levels of access to the database. Our ideal candidate holds a PhD in neuroinformatics, data science or a related field, and has a keen interest in developing a research
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for modeling and prediction. Our research is based on statistical machine learning and signal processing, on quantitative analysis of digital media and text, on mobility and complex networks, and on cognitive
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at DTU and will work closely with researchers from the Royal Danish Academy). Qualifications As a formal qualification, candidates must hold a PhD degree at the starting time of the PostDoc. Candidates
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research-based teaching portfolio at the bachelor, master, and PhD levels. You will be responsible for the teaching of courses. DTU employs two working languages: Danish and English. Master and PhD courses
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languages (eg, R, Python), implement statistical associative models (eg, GLMM), as well as experienced in simulation development (eg, multi-agent based models). You will also work with stakeholder engagement
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these signals offer for modeling and prediction. Our research is based on statistical machine learning and signal processing, on quantitative analysis of digital media and text, on mobility and complex networks
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. The ideal candidate is expected to have a very strong background in machine learning and must have a PhD in machine learning (or a closely related topic). Furthermore, the candidate must have experience with
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are seeking a candidate with a PhD in food science, food technology, or similar. As part of your research, you will: Use different supercritical CO2 and sub-critical water extraction technologies as well as pre