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17 May 2024 Job Information Organisation/Company Universitat de Barcelona Department Organisation profile Research Field Physics Researcher Profile Recognised Researcher (R2) Country Spain Application Deadline 5 Jun 2024 - 23:59 (Europe/Madrid) Type of Contract Temporary Job...
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-benefit, and other economic evaluation techniques commonly used in health economics; (3) proficiency in statistical analysis and econometric methods to analyze healthcare data and interpret findings
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to extend the contract as part of other grants within the lab. The requirements for the position are: PhD degree in an area pertinent to the project, such as applied mathematics, statistics, machine learning
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collection. Analysis of the collected samples Statistical treatment of the data. Writing reports and manuscripts for publication Requirements Research FieldEducational sciencesEducation LevelPhD or equivalent
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chemical, geology, microbiology and economics. Knowledge of Gis and statistical analysis. Experience in groundwater and surface water contamination studies. To have worked in the Muga basin on water-related
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climate dynamics and statistical analysis. Specific Requirements Good skills at programming (python). Experience writing scientific articles and presenting results at international conferences
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Proficiency in curating and analyzing large datasets Proficiency in analyzing spatial data Strong statistical background Experience in fieldwork LanguagesENGLISHLevelExcellent Research FieldBiological
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programming Expertise in additional quantitative research methods (e.g. time-use analysis, system dynamics, machine learning, econometrics, advanced statistics, big data, material flows analysis, etc
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and monitoring fieldwork, (2) carry out in situ experiments to assess the recruitment of the model coral species, (3) statistically analyse data obtained, and (4) write reports and scientific research
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in any of the following areas are desirable (but optional): Probability and statistics. Mathematics and/or applied mathematics. Bias detection and correction. Transfer learning and domain adaptation