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support systems. Writing code using programming languages such as C#, C++, Python, Kotlin, Java or Swift. Developing and implementing APIs between various systems. Learning and gaining experience in game
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applications and end user experiences Writing code using programming languages such as C#, C++, Python, Kotlin, Java or Swift Developing and implementing APIs to write XR applications and use OpenXR standards
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support staff from the Sustainable Agricultural Water Systems ARS-USDA unit (https://www.ars.usda.gov/pacific-west-area/davis-ca/sustainable-agricultural-water-systems-research/ ). Research (90%): Field
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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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, 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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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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(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