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). - Has strong quantitative skills (analyses of long-term data, modelling and statistical analyses, particularly quantitative genetics, ideally in the R environment). - Is proficient in English and meets
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comparative and statistical approaches Exploring evolutionary hypotheses by clustering behavioural traits and mapping them onto phylogenetic trees Collaborating with a multidisciplinary team of biomechanists
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research methods, including (but not limited to) longitudinal survey research, (automated) content analysis, and experiments; proven proficiency in statistical software such as SPSS, Stata and/or R; a
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, (Behavioral) Data Science or a related field; The skills and knowledge for processing, preparing and statistically analyzing big data; The skills and knowledge for carrying out in depth empirical studies
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of econometric methods, statistical software (e.g., Stata or R), or experience with economic experiments is an advantage. Independent yet collaborative – You can work autonomously while thriving in a team-based
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, Engineering or related field. A strong background/knowledge in machine learning and computer vision, natural language processing is a plus. Solid mathematics foundations, especially statistics, calculus and
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, natural language processing is a plus. Solid mathematics foundations, especially statistics, calculus and linear algebra; Excellent programming skills, preferably in Python. Experience with AI/HPC
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analytics (statistical models, machine learning, uncertainty quantification) to monitor and predict cycling travel conditions from various perspectives (safety, crowding, travel time, comfort, etc
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field. Graduation from a two-year Master’s programme (120 ECTS) is an advantage. Research skills – Knowledge of econometric methods, statistical software (e.g., Stata or R), or experience with economic
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: Experience designing and conducting experiments and processing geological samples A theoretical background in thermodynamics, mineralogy, petrology and economic geology Statistics and coding experience A