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Field
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on individual paths and preferences. The PhD candidate will have the opportunity to develop unique datasets and apply quantitative and mixed-method approaches to address policy-relevant questions
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capability. Comprehensive education: Enjoy numerous opportunities for scientific training, skills development and problem solving. Diverse research areas: Work on numerical models, analyzing large data sets
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
parametric studies following a design of experiments and to generate databases of physical fields that can be used to train generic meta-models [McCabe et al., 2025]. Furthermore, since industrial equipment is
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, perform data analysis using advanced statistical methods, prepare scientific manuscripts, and participate in broader project activities such as synthesis, dissemination, and co-creation workshops. Active
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, or other quantitative data analysis, and some experience with statistical software packages; you are keen to develop these skills further. You possess strong and documented scientific writing skills. You
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Bourgogne (Dijon), to develop and apply mathematical results to better understand the diversity of your corpus and interpret this diversity in terms of networks, thereby shedding light on the circulation
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our website: https://www.list.lu/ How will you contribute? You will join the recently awarded PEARL project FUNG-FACTORY in a team of approximately 10 people – led by Ronald de Vries, dedicated
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well as their social and cultural determinants, with applications in key areas such as education, health and industry. At the interface between experimental psychology (social, cognitive and developmental) and
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the ARS Mayotte teams to present their findings and develop their research project. Additionally, they will have the opportunity to engage with national networks in mathematical epidemiology modeling
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to predict marine extremes for engineering and navigation purposes The PhD candidate is expected to develop an integrated framework that combines machine and deep learning methods with statistical and