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Field
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* Doctoral degree in Kinesiology, Psychology, Computer Science, Biomechanical Engineering, or a related field. A high degree of proficiency using R, Python, and/or Matlab to process and analyze time-series
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biological mechanisms utilizing U-M Core Facility resources. Experience in Python or R programming is highly preferred. Mission Alignment: Candidates must demonstrate a clear interest in ecology and species
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or TensorFlow. Advanced programming and high-performance computing skills, including proficiency in Python and/or C/C++, experience with GPU acceleration, and the ability to develop, test, and maintain research
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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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: 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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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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Knowledge of SAS, R, Python, and SQL, or data visualization software Knowledge of open/public/private databases HOW TO APPLY: Applicants should submit a letter of interest, curriculum vitae (CV), name
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coding languages, such as R and Python Willingness to learn new methods and pipeline development by interacting with others Willingness to help train others Ability to read literature and learn vision
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: language learning/acquisition, interpretability/mechanistic analysis, human-like modeling, or formal language theory Excellent programming and data analysis skills (Python required; familiarity with PyTorch