114 programming-"the"-"DAAD"-"IMPRS-ML"-"UCL"-"O.P"-"IDAEA-CSIC" positions at Leibniz in Germany
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
following requirements: strong background in geoinformatics, geography, environmental science, data science or related fields in natural science GIS software proficiency (QGIS or ArcGIS) programming
-
experience in NGS data analysis. relevant experience in statistical data analysis and programming (e.g. R, Python, Perl, C++) as well as with workflow management systems (e.g. snakemake, CWL, Nextflow
-
sense of responsibility Goal-oriented and independent way of working Also desirable are: Programming skills (preferably Python or Ruby) Knowledge in the administration of SAN infrastructures using Fiber
-
– company pension plan (ZVK) Senckenberg is committed to diversity. We benefit from the different expertise, perspectives and personalities of our staff and welcome every application from qualified candidates
-
– annual special payment – collectively agreed vacation entitlement – company pension plan Senckenberg is committed to diversity. We benefit from the different expertise, perspectives and personalities
-
coding experience with e.g. Python/Matlab/R Practical experience with High Performance Computing, and scientific programming and a willingness to learn to work with high-performing computing systems
-
countries Confident use of MS Office programs EU driving license class B We offer An attractive, interdisciplinary working environment in a mixed team of experienced and young scientists and technicians
-
your work. We expect you to provide a research plan with your application. Cell lines collection: Your working group will coordinate and supervise the genotyping service for internal and external
-
development knowledge of the state-of-the-art in climate physics and Earth system modeling and data analysis techniques programming skills e.g. in C, BASH, Python, Latex software user knowledge: MS or Libre
-
data. You bring first experience with biostatistics methods, e.g. with mixed-models. You are familiar with data analysis using programming languages like R, and/or Python. You have excellent