Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
statistical analysis of results Experience with collecting field data in a spatial context Insight into analyzing data-intensive measurements and sensitive analysis Who we are At the Department of Agroecology
-
there. There are also some postdoc stipends available which are not attached to any of the above mentioned centers/grants. Note that we also have a different call in Statistics, Insurance and Economics, including
-
computational chemistry or physics will be preferred, but candidates with a solid background in statistics, computer science, and/or mathematics are also encouraged to apply. Programming skills (e.g., Python
-
analytical skills in model evaluation Experience in advanced statistical analysis of results Experience with crop model calibration and evaluation, such as DSSAT, APSIM, or equivalent Insight into analyzing
-
experience Skills within statistical analysis Communication skills both in oral and written English and preferably also in Spanish Flexibility and self-motivation are desired skills at DTU We expect you to be
-
or laboratory analyses. Familiarity with statistical analyses and data integration across multiple sources. Collaborative skills and ability to demonstrate commitment in teams Motivation to pursue a scientific
-
or laboratory analyses. Familiarity with statistical analyses and data integration across multiple sources. Collaborative skills and ability to demonstrate commitment in teams Motivation to pursue a scientific
-
of metabarcoding data, plant metabolomics or transcriptomics, multivariate statistical analyses or soil microbiology Further, we will prefer candidates with some of the following qualifications: Teaching and
-
of supervision at Bachelor’s and Master's degree level. We expect you to have experience with human electrophysiology (i.e. MEG and EEG, in particular hyper scanning), expertise in advanced statistical analysis
-
approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modeling, aimed at uncover the key traits that define successful microbial biofertilizers