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competitions or research projects will be an advantage; Experience in software engineering, including designing, testing, and maintaining complex code systems (preferred languages: Python, C++, or others used in
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burn chamber. https://research.fs.usda.gov/firelab At the heart of the USDA Forest Service's mission is their purpose. Everything they do is intended to help sustain forests and grasslands for present
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in bioinformatics or genomic data analysis, and possess strong programming skills in languages commonly used in bioinformatics, such as Python and R. Candidates should also have experience with genomic
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. Proficiency in data analysis software (e.g., R, MATLAB, SPSS, Primer, Python). Experience with analytical techniques such as ANOVA, PCA, Regression, and Multidimensional Scaling (MDS). Experience in ecosystem
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/ experience in audiovisual immersive communications. Medium / high level in C/C++/C#, Python and Unity. Fluent Spanish and good written and spoken English. Interest in working in advanced audiovisual
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or Departmental Website: https://med.stanford.edu/michellelinlab.html (link is external) How to Submit Application Materials: Please send materials to PI Dr. Michelle Lin (mplin [at] stanford.edu) with the subject
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of postdoctoral experience or equivalent in using DFT for studying reaction mechanisms and materials. Demonstrated programming skills (Python, Fortran, C++). Proven track record of publishing scientific papers in
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the use of bioinformatics tools for metagenomic and transcriptomic data analysis (e.g., QIIME, DADA2, R, Python). Demonstrated ability to independently design and conduct experiments, analyze data, and
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applications (https://www.cbr.washington.edu/analysis) to perform statistical analyses relevant to fish, dam, water, and natural resource management. The Software Engineer will play a lead role in the
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Science, physics, mathematics, engineering or a related discipline Strong Python skills Experience with modern deep-learning architectures The ability to properly report, organize and publish research data