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statistical analysis and ecological data, preferably using R or related tools Good written and oral communication skills in English, and knowledge of a Scandinavian language will be an advantage In addition
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) Documented record of advanced quantitative methods skills in R and Python, specifically Experience with GIS and spatial data analysis Experience with natural language processing or text-as-data approaches
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within programming and statistical analyses (e.g., in Python, R, etc) are a requirement. A background in media technology & AI is a requirement, and knowledge in the centre’s research areas. The applicant
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, Atmospheric Science, Environmental Science, or related fields Good knowledge and skills in statistics and programming (e.g. R or Python) is required Experience with data analysis related to terrestrial ecology
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is supervised by Professor Amir R. Nejad and Professor Ole Andre Øiseth . Your immediate leader is Head of Department. Duties of the position Development of integrated condition monitoring system
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: Required qualifications: Experience with data analysis and familiarity with statistical software (e.g., R, Stata) The applicant must be fluent in oral and written English, see documentation requirements
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software (e.g., R, Stata) The applicant must be fluent in oral and written English, see documentation requirements Ability to work both independently and as part of a multidisciplinary and international team
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knowledge about natural resource management Knowledge of software R Strong skills and/or interest in mathematical and statistical modelling is a strength Ability to conduct field work in remote alpine areas
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. Experience with epidemiology and statistical analysis, including the use of statistical software such as STATA, R, or SAS, will be viewed positively. Documented or demonstrated ability to work independently
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STATA, R, or SAS, will be viewed positively. Documented or demonstrated ability to work independently with register data or large datasets will be considered an advantage. Experience with, or knowledge