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and validate them across multiple cancer cohorts Link CIN programs to outcomes and therapy response using large public datasets and modern predictive modeling Integrate CIN signatures with functional
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workflows and benchmark methods (simulation + real datasets) Develop rare-event–sensitive CIN/aneuploidy metrics and validate them across multiple cancer cohorts Link CIN programs to outcomes and therapy
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(preferably in R, Python, GIS) • Competences in quantitative research methods - ideally knowledge of several of the following aspects of quantitative data analysis: analysis of large/longitudinal datasets
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-authored peer reviewed papers) • Well-developed statistical software skills (preferably in R, Python, GIS) • Competences in quantitative research methods – ideally knowledge of several of the following
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) • Preferably demonstrable experience in academic writing for publication (e.g. first or co-authored peer reviewed papers) • Well-developed statistical software skills (preferably in R, Python, GIS