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journals and conferences; Contribute to educational activities; Write a dissertation. Selection Criteria A MSc degree in Computer Science, Statistics, Artificial Intelligence, Data Science, or a related
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psychology) or data analysis (e.g., data science, statistics) Affinity with data science (e.g., complex statistics, machine learning or computational modelling) or willingness to develop relevant skills (in
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related field. A keen interest in particle physics and data processing at high-energy physics experiments. A strong background in (experimental) particle physics and statistical data analysis is an added
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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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, Cascais, Riga, Vilnius, Melsungen, Ciampino, Urla and Rhodes. The PhD project will involve: The use of data analytics (statistical models, machine learning, uncertainty quantification) to monitor and
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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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, and data collection. Proficiency in using programming tools (e.g., Python, C#) and statistics to support your work. An open-minded personality with a curiosity-driven mindset, positive attitude towards
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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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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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organizing collective behaviour Analysing interspecific variation in swarming behaviour using comparative and statistical approaches Exploring evolutionary hypotheses by clustering behavioural traits and