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Field
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9 Mar 2026 Job Information Organisation/Company Université Paris Cité Department Center for Research in Epidemiology and Statistics (CRESS) Research Field Medical sciences » Epidemiology Biological
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. Qualifications Basic Qualifications: College background in psychology, social/political science, communications, economics, computer science, data science, statistics, or related field. Additional Qualifications
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equivalent, in bioinformatics, data science, computer science, computational biology, statistics, public health, biomedical engineering, applied mathematics, physics, or another quantitative field of relevance
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statistical methods are not suitable for big data due to their certain characteristics: heterogeneity, statistical biases, noise accumulations, spurious correlation, and incidental endogeneity. Therefore, big
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, REddyProc) Excellent numeric and analytical problem-solving skills, including time series analysis and statistics (e.g. mixed effects modelling) Capacity to develop computer code and experience with
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Basic data analysis and processing skills Statistical analysis knowledge and expertise MS Excel, MS Access, SQL, SAS Python, web-scraping and NLP Any programming experience is a plus Willingness to learn
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epidemiology. We are keen for this PhD project to be student-led and shaped and driven by the successful candidate’s research interests and skills. Techniques to be used: Epidemiology Statistics and data
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of domestic and foreign professional publications, scientific studies from the Web of Science and Scopus databases, relevant web resources, publicly available documents, and data from the Statistical Office of
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., PsychoPy, E-Prime, Gorilla, Presentation). Hands-on experience in data visualization, data analysis, and programming in R and/or Python. Experience in, and aptitude for, complex statistical modelling (inc
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advanced data science methods, including statistical modeling and machine learning approaches Developing and applying novel causal inference methods, such as target trial emulation and causal AI frameworks