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datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning). The goals are to develop new computational methods that allow the scientific inference
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of aging and lifespan development Interest in studies of ambulatory assessment Good knowledge of and interest in quantitative methods (e.g., multivariate analyses, longitudinal analyses, multilevel analyses
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of ambulatory assessment and very old age Good interpersonal skills and interest of working with older adults Good knowledge of and interest in longitudinal quantitative methods (e.g., multivariate analyses
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chemistry The ideal candidate will have a strong background in analytical chemistry, derivatization methods, extraction as well as gas chromatographic techniques. Further experience in the handling of extra
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. The project is led by Heiko Schütt and will employ one PostDoc and one PhD student. About the role... You will develop new Bayesian methods to compare deep neural network and other artificial representations
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. To do this, knowledge or willingness to be trained in advanced statistical modelling, ideally with an interest in methods for causal inference in observational data, is strongly preferred. Using various
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research and command of quantitative and / or qualitative empirical methods; openness to mixed methods research Readiness to collaborate with local and international stakeholders and in outreach activities
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short literature review, an overview over the methods and data that you aim to use The names of two referees Early application is highly encouraged, as the applications will be processed upon reception