Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. Alongside developing own research ideas, applicants should be capable of turning those ideas
-
models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. The evaluation of applicants primarily hinges on their documented academic qualifications and
-
. Computational & Data Analysis: Proficiency with software such as FlowJo and GraphPad Prism is expected. Experience analyzing large-scale datasets (e.g., scRNA-seq, TCR/BCR sequencing) and familiarity with R
-
NOKUT) Candidates who already hold a PhD will not be considered for the position In the assessment and ranking of qualified applicants, emphasis will be placed on: Experience with relevant R&D work
-
microbiology, or environmental microbiology. Strong programming/computing skills (e.g., Python and/or R), Unix/Linux command line, and good practices with Git and reproducible research. Experience using
-
modelling, preferably in R Fluent oral and written communication skills in English. Relevant and significant scientific publications in respected journals, at the level of the career status of the applicant
-
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
-
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