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post) Preferably demonstrable experience in academic writing for publication Well-developed statistical software skills (preferably in R, Python, Stata) Full endorsement of current open science practices
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equivalent qualifications Expertise in one or multiple of the following areas: Machine Learning, Computer Vision, Image Processing, (Aerial) Robotics Excellent programming skills in Python (possibly also in C
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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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(at least in Python), familiarity with workflow automation is an advantage Knowledge of statistical concepts and quality control Excellent communication, documentation, and data visualization skills Very good
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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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software skills (preferably in R, Python, Stata) Full endorsement of current open science practices Communication and teamwork skills, commitment, autonomous working style, attention to detail, willingness
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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