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assembly, population genetics, statistical genetics, complex trait mapping, and high throughput sequencing genome and programming proficiency in R, Python, Perl, C/C++, Java, and SAS are highly desirable
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, or unsupervised learning methods. Proficiency in Python and familiarity with scientific computing libraries such as PyTorch, TensorFlow, Pandas, NumPy, and related ML frameworks. Experience with large datasets
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approaches. Familiar with common scientific programming languages such as python, C++, or Fortran. Experience with high performance computing environments is welcome. Candidates who do not strictly meet these
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of large-scale genomic and transcriptomic datasets ('big data'), with hands-on experience in high-performance computing (HPC) environments (e.g., command-line interface, scripting in R/Python, use of common
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of the relevant fields. Preferred skills: Previous experience in computational ecology and statistics. R or Python. Statistical analysis tools such as NIMBLE, JAGS or STAN. Familiarity with data processing, quality
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) data sets in R or Python as well as GIS software such as QGIS or ArcGIS Pro. Knowledge of US forest ecosystems and background in analyzing forest structure, stand dynamics, biodiversity, and/or other
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pursuing a bachelor's degree in the one of the relevant fields anticipated to be received by May 31, 2027. Preferred skills: Experience with fire management, landscape ecology, programming (e.g., R or Python
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, Geoscience, Soil Science, Physics, or related fields). Preferred skills: Commitment to contributing to outreach activities and research dissemination. Experience in computer programming (R+, Matlab, Python
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(e.g., command-line interface, scripting in R/Python, use of common bioinformatics software). Strong foundation in experimental design, advanced statistical analysis, and scientific writing. Proven
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/statistical skills Experience with statistical analysis software (e.g. R, SAS), GIS software (e.g., ArcGIS, QGis), and programming languages (e.g., R, Python, C++). Experience or interest in science integration