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programming skills in R. Proficiency working within UNIX/Linux environments. Preferred Qualifications Experience with Bayesian statistical methods. Experience with hierarchical modeling and mixed effects models
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). Candidates must have excellent quantitative, writing and communication skills. Expertise in R, Matlab programming, Linux computing, and familiarity with neuroimaging software tools (FSL, AFNI, SPM) is
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assembly, annotation and phylogenetic or pangenomic analysis, and be confident working in Linux and high-performance computing environments using R, Python, or equivalent tools. You will also need a strong
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or similar is therefore required • A substantial fraction of the technical part will be handled on high performance computers and the AI supercomputer GEFION, all operated in a Linux Environment. Documented
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data assimilation systems on Unix/Linux and high-performance computing platforms. Evaluate model and assimilation performance using statistical and dynamical diagnostics and verification against
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Python and experience working in Linux and high-performance-computing environments. Experience developing or using automated and reproducible computational research workflows. Ability to conduct
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biological or genomic datasets. Proficiency in programming languages commonly used in scientific computing (e.g., R, Python, Linux/Unix environment). Excellent analytical, written, and verbal communication
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discipline. They should have strong computational skills, including experience with UNIX/Linux and programming in Fortran, Python, or other high-level languages. Candidates should also demonstrate the ability
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(e.g. Linux, R programming) is essential. The appointees will need to perform data analysis of single cell RNA-sequencing, transcriptomics data, Nanopore long read sequencing analysis and/or
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approaches for learning from molecular or physical systems. Ability to develop reliable research software in a Linux environment using version control, testing, documentation, and reproducible computational