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-omics analyses is an advantage; Basic proficiency in data analysis using R, Python, and/or Matlab, with a strong interest in computational methods, including AI-enhanced analyses; Excellent skills in
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related discipline; An interest in the impacts of land use; Experience in handling spatial data; Programming experience in Python, and/or R; Proficiency in English; and The ability to work as part of
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audiences and collaborate across disciplines; proficiency in spoken and written English. Experience with plant pathogens, microbiome sequencing, image-based phenotyping, R/Python, statistics or machine
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competence in quantitative and statistical methods, including applied econometrics and panel data analysis, proficiency in a statistical programming language such as R (preferably), Python, or Stata. Prior
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and statistical methods, including applied econometrics and panel data analysis, proficiency in a statistical programming language such as R (preferably), Python, or Stata. Prior exposure to firm-level
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strong affinity for language data; solid programming skills (e.g., Python) and experience with machine learning or NLP, ideally including transformer-based models and word embeddings; excellent English
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scale datasets; Programming skills in Python and/or R; familiar with reproducible coding and automated (geospatial) data analysis; Familiarity or interest to dive into environmental or soil science
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Holocene lowlands, and translation them into subsurface maps, sections, and schematizations; Proficiency in numerical groundwater modelling, including the use of coding (e.g. Python) and incorporation