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Field
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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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proficiency in coding, at least using Bash and Python. Applicants should maintain their code in a public repository (e.g. GitHub) and include the link in their application. Proven skills in Linux/HPC
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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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. Experience working in Linux environments, including batch job management on shared computing resources. Familiarity with a variety of supervised and unsupervised classification techniques. Proficiency in one
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, pandas, scikit-learn), Linux, and Git. Proven ability to develop well-documented, maintainable research software and manage experimental datasets. Strong publication record relative to career stage
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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
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computing (Linux) environments, including shell scripting. Strong programming and data analysis skills (e.g., Python, Fortran, R). Demonstrated ability of scholarly output (peer-reviewed publications and
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must have strong foundation in statistics, including multivariate statistical analysis and appropriate validation of quantitative models. The candidate must have experience working in Linux or Unix-based