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education in computer science or a related field (e.g. mathematics, statistics, geoscience with strong emphasis on computational and programming aspects), or must have submitted his/her master's thesis
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. Qualifications and personal qualities The must hold a master's degree or equivalent education in computer science or a related field (e.g. mathematics, statistics, geoscience with strong emphasis on computational
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passerine birds is a requirement. Good statistical skills and familiarity with R programming is required. Experience with the monitoring of breeding birds in nestbox populations throughout at least one entire
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has been awarded. Experience with handling and measuring small passerine birds is a requirement. Good statistical skills and familiarity with R programming is required. Experience with the monitoring
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strong background in statistics is required. Experience in atmospheric dynamics or climate dynamics, basic shell scripting, and python/Matlab/R or similar languages is required. Experience with AI-related
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the master's degree has been awarded. Experience with handling and measuring small passerine birds is a requirement. Good statistical skills and familiarity with R programming is required. Experience with
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statistics is required. Experience in atmospheric dynamics or climate dynamics, basic shell scripting, and python/Matlab/R or similar languages is required. Experience with AI-related research and/or
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or several of the following different aspects of Reeb graph learning: mathematical foundations, (probabilistic) learning algorithms, performance measures, metrics, visualization, statistics, downstream tasks
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for the duration of the fellowship. A strong background in statistics is required. Experience in atmospheric dynamics or climate dynamics, basic shell scripting, and python/Matlab/R or similar languages is required
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learning: mathematical foundations, (probabilistic) learning algorithms, performance measures, metrics, visualization, statistics, downstream tasks such as dimensionality reduction or generative modelling