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designing machine learning pipelines, building web applications or tools, and creating and maintaining visualization dashboards. Trainees should be comfortable with: · SQL, R, and Python
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related to the microbiome/metabolome. Superb quantitative background, strong coding skills (e.g., Python, R, MATLAB). Strong computer skills, experience with databases and scientific applications, and
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Matlab, Python, and/or R programming languages is a plus. Knowledge, Skills and Attitudes: Skill in collecting, organizing, and analyzing data. Analysis of fMRI and MR spectroscopy data. Acquiring
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computational background to analyze sequencing, high dimensionality data, and be efficient with bash, python, and/or R. Expertise with genome-wide methylation datasets, next-generation sequencing, related
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experience working with drone, SkySat, PlanetLab, and/or Sentinel 2 data, using R and Python, and doing disease ecology modeling will be assessed favorably. Required Application Materials: 1. Curriculum vitae
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. Proficiency in scientific programming (e.g., R or Python) and scripting in research environments is required. Substantial experience with SPM and connectivity toolboxes is highly desirable. Required Application
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biology, computer science, engineering, biophysics, applied mathematics, or related field. Computer programming expertise (e.g., Python, Matlab, or TensorFlow) is required. Strong interest in
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degree with training or research experience in epidemiology, population genetics, and/or computer science Strong coding skills for the data sciences (R, python, etc) Excellent English writing and speaking
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low-input sequencing workflows Experience analyzing genomics datasets in Python or R Ability to work across engineering and biological disciplines Stanford is an equal opportunity employer and all
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(Python, PyTorch/TensorFlow, radiomics, 3D Slicer, DICOM tooling). We foster a commitment to open science practices, rigorous validation, and pathways for translating methods into practice. ALTERNATIVE