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, and python/Matlab/R or similar languages. Experience with “traditional” climate modelling, data-driven climate modelling, and working with large ensembles of climate/weather model output are advantages
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climate change are far-reaching, particularly when it comes to identifying and interpreting trends in regional-to-local scale signals and extreme events. Large ensembles of climate simulations are a key
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primary focus on wind energy. It examines how climate change affects their efficiency and reliability and how large-scale systems impact regional or larger-scale climates. The candidate will collaborate
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their efficiency and reliability and how large-scale systems impact regional or larger-scale climates. The candidate will collaborate with GFI's energy and climate groups on this research. The project aims to assess
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RNAseq library prep), large scale data analysis. The candidate will contribute to the design and execution of experiments, data analysis, and dissemination of results in peer-reviewed journals and
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, immunohistochemistry, mouse colony administration, genotyping, confocal and large scale microscopy, molecular biology techniques (RNA preparation, single cell RNAseq library prep), large scale data analysis
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analyses of data from large patient cohorts. The ideal candidate publishes at a high international level, including several first- and last author publications without co-authorship of PhD or postdoc
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aerodynamics. Experience in data analysis and interpretation of large datasets. Knowledge and experience from use and development of CFD codes. Risk analysis and risk assessments in the process industry. Good
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. Cantera or CHEMKIN. Background in compressible flows and applied aerodynamics. Experience in data analysis and interpretation of large datasets. Knowledge and experience from use and development of CFD
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practical experience in data science applied to medical or population genomics or other omic demonstrate experience in analyzing large omic data be proficient in one programming language be able to work