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pathways for the required paradigm shift to sustainability. This position focuses on the initial work package in the project, to conduct statistical topic modelling on policy documents, ideally across
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Identification of soil invertebrates (e.g. mites, springtails, insects) using modern and classical techniques Laboratory analyses of soil properties Statistical analysis of complex ecological datasets Presentation
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) assess future changes in these patterns under different global warming scenarios. Requirements: The successful applicant should hold a MSc or PhD degree in physics, mathematics/statistics, climate science
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track record in data modelling, machine learning and deep learning Previous research achievements supported by peer-reviewed publications Excellent knowledge of statistical/machine-learning and deep
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alignment and bioinformatics analyses Integrate computational, laboratory, and fieldwork approaches to study population genetics Develop and apply statistical and computational models for evolutionary
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) and programming languages (e.g. Python, Matlab, R) as well as in advanced statistical methods for analyzing complex ecosystem and environmental datasets. Good knowledge of European marine ecosystems as
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., deep learning and statistical modeling). You have knowledge of molecular genetics and genomics. You have a very good command of English (both spoken and written). You have the proven ability to conduct
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skills in statistical analyses, preferably using R Strong track record of international publications Excellent written and oral communication and project presentation skills in English Salary and benefits
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series of turbidity, light attenuation, Secchi depth and photosynthetically active radiation (PAR) based on in situ, remotely sensed and modelled data. Conducting a statistical ensemble analysis of
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criteria: Proven track record of publishing high-quality scientific articles in peer-reviewed journals. Excellent command of written and spoken English. Proficiency with advanced statistical methods