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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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statistical methods for genetic and genomic data analysis; proven ability to build and maintain collaborative networks across academia, industry, and international partners; strong organizational skills and the
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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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Institut de Recherche pour le Développement (IRD) | Montpellier, Languedoc Roussillon | France | 2 days ago
Optional Skills / Knowledge Expertise in statistics Expertise in high-performance and parallel computing Expertise in neural networks Personal Qualities Intellectual curiosity Autonomy Adaptability Rigor
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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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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
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outcomes research and real-world data analytics, with a strong publication record. Proficiency in advanced data analytics, machine learning, and statistical modeling. Job Description: The Department
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in statistics and stochastic modeling with applicability in microeconomics, macroeconomie and sustainability are particularly welcome, Experience in previous similar projects will be considered a major
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University-led dairy cattle project. Working closely with researchers across disciplines, you will integrate diverse datasets, conduct statistical analyses, and communicate findings to a range of audiences
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language; b) Strong knowledge in the application of statistical methods in analytical epidemiology, applied to cohort studies; c) Solid knowledge of Geographic Information Systems (GIS), applied