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Field
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potentials or force fields (for example MACE, NequIP, DeePMD). ● Experience running scientific calculations on Linux-based high-performance computing clusters, including scripting or workflow automation
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computing tools in a Linux or Windows environment. Experience with scientific data visualization and preparation of figures for presentations or publications. Graphic design experience and the ability
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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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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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programming skills in Python and/or R; experience with SQL and Linux/Unix is desirable. • Demonstrated research experience in biomedical informatics, artificial intelligence (AI), machine learning, or data
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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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language. Working knowledge of UNIX or Linux. Preferred Knowledge, Skills, and Experience Experience with machine learning and accelerator operation. Experience working with complex algorithms. Experience
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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
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demonstrated through research experience and relevant publications. Candidates with demonstrated experience of working with fMRIprep pipelines will be given higher preference. Familiarity with Linux Operation