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, Python, Perl, Go Virtualization - ESXi, kvm, XCP-NG, or an equivalent combination of education and experience. Desired Qualifications In addition to the core skills described above, expertise
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managing physiological data, environmental sensor data, or high-throughput phenotyping data. Experience with basic data organization, spreadsheets, R, Python, or other data analysis tools. Experience with
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Earned Ph.D. in Biosystems Engineering, Food Science, or equivalent Demonstrated expertise in computational modeling Demonstrated experience coding in multiple programming languages (R, Python, MATLAB
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, computation, design, and data analytics with experience in structured programming and data visualization (such as MATLAB, Python, Tableau, or Power BI). Preference will be given to candidates with
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. Significant experience using Python for high performance applications with libraries such as: Numpy, Numba, CuPy, and PyQtGraph Significant experience using C++ for numerical computing applications
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maintaining applications using Python, JavaScript, Arcade, SQL, and PostgreSQL, including database development, integration, and performance optimization. Experience designing and implementing complex data
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downloading, organizing, documenting, quality-controlling, analyzing, and interpreting time-series or spatial monitoring data using Python, R, MATLAB, or comparable analytical tools Excellent written and
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methods; Proficiency in statistical software (e.g., SPSS, SAS, R, and/or Python); Ability to use diverse technologies to support learning, teaching, grant proposal submissions, and research; Excellent
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software(s), proficient in programming in both R and/or Python. Ability to collaborate and clearly communicate with stakeholders, state and federal agency biologists, university scientists, and non
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solve research problems. Strong technical and problem-solving skills, including Linux command line experience and programming experience with a modern language such as R, Python, or C++. Strong verbal