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, multivariate calculus, and the postulates of quantum mechanics. Desirable expertise includes mathematical optimisation theory (constrained continuous optimisation), and prior experience in quantum error
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background in micro/nanofabrication, characterization and the development of miniaturized devices. Experience with multivariate analysis, computational methods or statistical techniques is highly desirable
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the dissolution of peptide powder formulations using in-situ UV spectroscopy. The candidate should have previous experience collecting, structuring, and analysing spectroscopic data using multivariate analysis
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Facility (ViSp) is a central infrastructure for this project (https://www.umu.se/en/research/infrastructure/visp/ ). The scholarship (30 000 sek/month) is funded by the Carl Trygger Foundation and the
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engineering, computational biology, biostatistics, or a related discipline, with a strong background in clinical data analysis and predictive modeling. Experience with multivariate regression techniques
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. Extensive knowledge of statistical methods including multivariate and univariate analysis of large data sets, learning and predictive modeling, network analysis, and probabilistic approaches to test theories
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multivariate analyses, particularly regression analyses (linear, logistic, multinomial, Poisson). Experience with data management, production of ongoing data reporting and graphics/table creation Experience
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Number AE2026-0029 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0029.pdf CALL FOR GRANT
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, or scientific publications Experience in statistical analysis of data including univariate, multivariate statistics Science communication skills proven publication record in international peer-reviewed journals
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Statistics (Ref: FST241106) Job Description Candidates with expertise in one or more of the following areas: Linear Algebra, Calculus, Statistics and Probability, Regression Analysis, Multivariate Analysis