17 python-"Diamond-Light-Source"-"Diamond-Light-Source"-"Diamond-Light-Source" Fellowship positions at Zintellect
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: Build reproducible workflows, functions, and R or Python packages; develop visualization tools and open-source applications; and share code through collaborative platforms such as GitHub. Scientific
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skills: Experience in statistical programming in languages such as R (ideal) or Python. An understanding of linear models and probability. A background in biology, with a preference for cellular biology
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robotics) for field-based phenotyping Data management and analytics from multi-stream remote sensing platforms Preferred willingness to learn: Use of Python, CRBasic, Matlab, C++, R, or other programming
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by 9/1/2026. Preferred skills: Experience in process-based crop, soil, or hydrological modeling. Proficiency in Python and other programming languages (Fortran and C/C++ are a plus). Familiarity with
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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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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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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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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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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