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, neuroscience, or a related field is required. Cognitive neuroscience expertise and a track record of academic publications are required, and expertise in advanced statistical methods involving HLM is highly
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background in statistics, and familiarity with large data sets such as metabolomics or epigenetic sequencing. Experience in epigenetics or gene regulation is a plus. Experience with statistical analysis tools
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(for example, the distance between the sensors) or based on statistical properties of the measured data (for example, the correlation between the measurements of the different sensors) [2]. Graph-based learning
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advancing the science underpinning sustainable, healthy and liveable cities of the future. This frontier research employs largescale digital datasets, artificial intelligence and statistical methods
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to daily lab operations. Knowledge, Skills & Abilities: Excellent laboratory and analytical skills and commitment to scientific excellence and innovation. Knowledge of statistics and data analysis and
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and follow-up targets Contribute to and lead dissemination activities, including leading/overseeing statistical analyses and preparing peer-reviewed journal publications and conference presentations
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Staphylococcus aureus biology and pathogenesis using in vitro and in vivo models. ● Document data generated in studies, analyze data with appropriate statistical methods, prepare manuscripts for publication in
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Psychology, Neuroscience, Cognitive Science or equivalent, with 3 years’ related work experience, or a Ph.D. degree in a relevant discipline; (ii) solid statistical and computing skills; (iii) effective
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Max Planck Institute for Political and Social Science, Göttingen | Gottingen, Niedersachsen | Germany | 10 days ago
obtained before the employment start date; Experience in conducting high-quality quantitative research, with a strong command of statistical analysis of survey data, statistical programming (R/Stata), and
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surveillance and preparedness planning using multiple modeling approaches. The successful candidate will develop and implement statistical and machine-learning models, integrate multi-source ecological datasets