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or integrated pest management with hands-on experience in agricultural field trials solid knowledge of statistical analysis and publication of research results first experience in acquiring third-party funding is
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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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and skills: You hold a PhD in Bioinformatics, Computational Biology, Genomics or a related field. You bring proven expertise in deep learning and statistical modelling of biological data. You have
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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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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
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geographic information systems (GIS) and programming languages (e.g. Python, Matlab, R) as well as in advanced statistical methods for analyzing complex ecosystem and environmental datasets. Good knowledge