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This position is embedded in the AIR-MoPSy project (Atmospheric Impact on the R-Mode Positioning System), which supports the development of a terrestrial backup to satellite-based navigation systems. GNSS (Global
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phenotyping, including image analysis evaluations, for trait quantification Handle NGS datasets for RNAseq or SNP detection and linkage analysis using R Your qualifications and skills: You have a PhD or
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Excellent skills in statistical modelling, preferably using R Proven track record of publishing in international peer-reviewed journals as first author Willingness to conduct fieldwork and participate in
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data science tools, especially Python or R Proven ability to work collaboratively in interdisciplinary and international research settings Strong skills in scientific project management Excellent written
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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
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chronometabolic research Analysis and quantification of circadian rhythms by rhythm analysis software (Cosinor, JTC cycle, R etc.) Rhythm analysis in human 24-hour time series data and omics data Writing
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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
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R documented excellent and intuitive graphical presentation of data analyses for presentation and publication excellent English language skills (speaking and writing) comprehensive presentation
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data using MEFISTO. Nature Methods (2022) Kleshchevnikov, Vitalii, et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nature Biotechnology (2022) Argelaguet, R., et al. Multi
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PostDoc in "Sustaining the keystone: Rethinking Antarctic krill fishery management under climate ...
ecosystems. Your Profile A PhD in marine biology, conservation biology, fishery management & conservation, or related fields A strong background in handling large data sets, programming (preferably in R