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cultures. Strong knowledge of quantitative methods and statistics, including programming in commonly used languages such as R. Very good oral and written proficiency in English. Assessment criteria and other
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research projects, as evidenced by scientific publications in internationally peer-reviewed journals - Good knowledge in genomics, bioinformatics and experience in statistical analysis of large datasets
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bioinformatics, physics, statistics, computer science, computational biology, or related fields. Experience programming in Python (or R) as well as bash/shell scripting. Experience with machine-learning and deep
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of atmospheric composition datasets. Experience in the use of and developing code in Python for manipulation, statistical analysis and presentation of atmospheric data. Consideration will also be given to good
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into consideration. The applicant should have good knowledge of automatic control, optimal control, statistics, and optimization. Furthermore, good programming skills are required, including Python and C++, and
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to formalize a biological question into a computational model. Merits: Computational genomics, medical statistics, computational biology and/or imaging analysis. Experience with NGS (10x platform, Smart-Seq
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skills in English Documented ability to independently drive research and publish scientific results Good ability to work in teams Personal suitability Merits: Research experience on Bayesian statistics and
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these datasets to detect chromosomal abnormalities and study their breakpoints. Using statistical methods and machine learning, we will explore how these structural variants arise and which recurring structures
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monitoring will be particularly useful. A solid background in statistical data analysis and previous experiences with catchment hydrochemical modelling are merits. Excellent English skills, both in speaking
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publish scientific results Good ability to work in teams Personal suitability Merits: Research experience on Bayesian statistics and model adequacy Experience with development of R packages Familiarity with