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Statistical results of empirical studies in the behavioral and social sciences have repeatedly been shown to be reported in a suboptimal way. This PhD project seeks to understand why applied
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partners. Strong applicants typically have backgrounds in computer science, statistics or econometrics but should have an intrinsic interest for marketing problems. The PhD will be supervised by Prof. Dr
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, such as statistical AI (machine learning), symbolic AI (knowledge graphs, reasoning), and human computation (crowdsourcing). By analyzing empirical evidence of human interactions with data and systems, we
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language proficiency that fits this target group, or affinity with the target group, is a plus. Besides, experience with statistical analyses (e.g., in SPSS and/or R) and writing scientific publications
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; study design; multidisciplinary analyses (e.g. Genetics, Behaviour, Isotopes); statistical analyses; academic writing. The project is supervised by Dr Lysanne Snijders (Behavioural Ecology group) and Dr
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strong background and solid hands-on experience in experimental analytical methods, data processing, and statistical analysis; a strong interest (or preferentially: experience) in spectroscopy, microscopy
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experimental research, including knowledge of and demonstrable experience with applying (state-of-the-art) statistical techniques. Experience with data wrangling (e.g., in R or Phyton). Open-minded and motivated
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of Dutch; interest in working on interdisciplinary topics, in interdisciplinary teams. Candidates, who have: software engineering skills quantitative skills, including survey development, statistical