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- Nova School of Business and Economics
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ongoing CoLAB projects. h) Analyzing and producing statistical indicators on tourism and hotel activity. Requirements Research FieldManagement sciencesEducation LevelPhD or equivalent
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algorithms; experience in statistical analysis and machine learning tools; experience in executing experimental protocols. Applicants eligibility: Applicants must comply with the eligibility conditions laid
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the overall problem a challenge, both from a statistical and computational point of view. In this context, artificial intelligence and machine learning offer a great opportunity for the effective quantification
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for characterization of cells (flow cytometry, metabolism, immunofluorescence microscopy); 4)Microscopy image analysis; 5) Data analysis using statistical tools. Proficient use of the English language, both spoken and
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techniques for environmental sciences – information provided in the CV and/or motivation letter; e) Experience in statistical analysis, preferably in an R environment – information provided in the CV and/or
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. Preference will be given to candidates with previous experience in conducting studies in the field of implicit learning using statistical learning paradigms; Proven expertise in experimental programming (e.g
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Universidade Lusófona´s Research Center for Digital Human-Environment Interaction Lab | Portugal | about 2 months ago
in developmental neuropsychology research methods; d) Fluency of both Portuguese and English languages (spoken and written); e) Fundamental knowledge of statistical software (e.g., SPSS, STATA); f
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. Data processing and statistical analysis; vi. Participation in the preparation of the Project’s final report. 4. Duration and Renovation conditions The fellowship, which is expected to begin in June
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WORK PLAN: ………………………………………………………………….……………………….. · Database cleaning and organizing · Initial statistical treatment and regression analysis · Support in the writing of articles
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Requirements: Master’s degree in economics or in life and health sciences. Enrolled in an academic or non-academic degree-granting course. Experience in data analysis and statistics, demonstrated by a curriculum