Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
analysis is a requirement Experience in using relevant software to perform complex tasks, e.g. R, ArcGIS, and Python is a requirement Experience in the mapping and modelling of ecosystem services is an
-
requirement Experience in using relevant software to perform complex tasks, e.g. R, ArcGIS, and Python is a requirement Experience in the mapping and modelling of ecosystem services is an advantage Knowledge
-
from R&D work and/or scientific publications Ability to work independently, purposefully, and systematically Ability to collaborate with others Motivation for research stays abroad (3–12 months) Interest
-
CT core scanning, as well as grain size analysis) is a requirement. Experience with (geostatistical) data analysis approaches (at least Excel and ArcGIS, but preferably also R and Grapher or similar
-
relevant programming languages (e.g., Python, MATLAB, R) is a requirement. Familiarity with downscaling and bias correction of climate data (e.g., from CMIP/PMIP) is an advantage. Experience with
-
science, is a requirement Applicants must possess strong skills in the management and analysis of ecological or biodiversity data using R. Experience (for example, a master’s project or internship) working
-
., Python, R, bash). At least one publication in an international peer-reviewed journal of an end-to-end software developed by the candidate Documented experience with Nextflow or Snakemake. Documented
-
analysis of ecological or biodiversity data using R. Experience (for example, a master’s project or internship) working with plant, vegetation, or alpine ecology is a requirement. Fieldwork experience and
-
(geostatistical) data analysis approaches (at least Excel and ArcGIS, but preferably also R and Grapher or similar) is a requirement Strong skills in statistical analysis and the handling of large spatiotemporal
-
. Proficiency in relevant programming languages (e.g., Python, MATLAB, R) is a requirement. Familiarity with downscaling and bias correction of climate data (e.g., from CMIP/PMIP) is an advantage. Experience with