Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- University of Oslo
- University of Bergen
- Nord University
- NTNU - Norwegian University of Science and Technology
- OsloMet
- University of Agder
- Østfold University College
- ; University of Oxford
- Norwegian Center for Violence and Trauma Stress Studies
- Norwegian University of Life Sciences (NMBU)
- UNIS
- University of Inland Norway
- 2 more »
- « less
-
Field
-
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
-
., 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
-
STATA, R, or SAS, will be viewed positively. Documented or demonstrated ability to work independently with register data or large datasets will be considered an advantage. Experience with, or knowledge
-
. Experience with quantitative cell biology and computational modelling, especially using R, is an advantage. Experience with applied statistics is an advantage. Applicants must be able to work independently and
-
knowledge about natural resource management Knowledge of software R Strong skills and/or interest in mathematical and statistical modelling is a strength Ability to conduct field work in remote alpine areas
-
backgrounds are essential. Good written and spoken skills in English are essential. Successful applicants are expected to have experience in molecular genetics wet-lab work. Basic skills in R and strong
-
research, combining laboratory and computational work. Experience with live-cell confocal microscopy is an advantage. Experience with quantitative cell biology and computational modelling, especially using R
-
estimators, or machine learning) or other advanced statistical modelling. Advanced programming skills in Stata, R, Python or a similar software. Strong academic background with publications in international