-
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
-
; Experience in handling spatial data; Programming experience in Python, and/or R; Proficiency in English; and The ability to work as part of an interdisciplinary research team. The following qualifications
-
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
-
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
-
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
-
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
-
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
-
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