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production of bacteria via mass spectrometry Statistical analysis of the resulting data The successful candidate will have the opportunity to work towards a PhD Required qualifications: University degree (M.Sc
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social scientists from a range of Leibniz research institutes in a new multi-year research project on antimicrobial resistance (AMR), including statistical and econometric investigations of country-level
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and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team
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user experience and learning effectiveness Contributing to research activities relevant to ASSURED, e.g., conference and journal papers on topics such as data governance, privacy and statistical
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, agricultural sciences with a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a
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, political violence, and transnational threats to peace and conflict. Strong method skills, including in comparative research or statistical methods. Willingness to work in an interdisciplinary and
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experience in the use of mass spectrometry • Good working knowledge in the application of statistical analysis procedures • Sound knowledge of ecological and evolutionary concepts • Basic knowledge in the use
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of audiovisual psychophysical studies with electroencephalography (EEG) and functional magnetic resonance imaging (fMRI)•Statistical analysis, computational modelling and presentation of behavioural, EEG data and
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experience in the use of mass spectrometry • Good working knowledge in the application of statistical analysis procedures • Sound knowledge of ecological and evolutionary concepts • Basic knowledge in the use
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analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team