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(IRT) models in small samples. The ideal candidate has prior knowledge of IRT models, a basic understanding of common estimation methods, and strong programming skills in R, Python, or another relevant
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knowledge of IRT models, a basic understanding of common estimation methods, and strong programming skills in R, Python, or another relevant computing language. Experience with machine learning methods is a
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programming language R Fluent oral and written communication skills in English The following qualifications are not required but will give applicants an advantage The quality of the project description and the
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to the contact person no later than June 20th. Advanced quantitative research skills Familiarity with the statistical programming language R Fluent oral and written communication skills in English The following
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with methods for causal inference Familiarity with administrative register data or other types of big data Familiarity with Stata, R, or other relevant computing languages Personal skills A collaborative
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to work independently and collaboratively in multidisciplinary teams Desired qualifications Crispr Crispr / siRNA screens Proficiency in programming (e.g., Python, R) Experience with high-throughput
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data Familiarity with Stata, R, or other relevant computing languages Personal skills A collaborative, friendly, and team-oriented style of work Ability to join interdisciplinary academic communities
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experience with designing experimental studies and analysing quantitative data. Proficiency in either R or Python. Favourable qualifications (not requirements, but give applicants an advantage): Relevant
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qualifications Crispr Crispr / siRNA screens Proficiency in programming (e.g., Python, R) Experience with high-throughput sequencing data analysis (e.g., CAGE, ATAC-seq, ChIP-seq, or Hi-C) Familiarity with
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with designing experimental studies and analysing quantitative data. Proficiency in either R or Python. Favourable qualifications (not requirements, but give applicants an advantage): Relevant