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analysis techniques for biomarkers and statistical analysis using Python. The candidate will be expected to lead research on using biomarkers to reconstruct past climate changes, including presenting
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accessing APIs using either Python/FastAPI or R/Plumber. Use of version control systems such as Git. Strong computer skills including experience with database management systems (MariaDB, MySQL, PostgreSQL
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interdisciplinary team. Excellent programming skills and proficiency in a programming language (e.g., C++, Python, MATLAB) and modern machine learning packages. In-depth theoretical and practical knowledge in
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, students, and external partners. Contribute to teaching, mentoring, and curriculum development. Knowledge, Skills, and Abilities Strong background in AI, GNC, and SDA. Demonstrated proficiency in C++, Python
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++, Python, MATLAB, and modern machine learning frameworks. Proven research record, including peer-reviewed publications. Excellent communication and collaboration skills. Minimum Qualifications Ph.D. in
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statistical and quantitative analysis, and programming ability in python. Minimum Qualifications PhD in hydrology, remote sensing or a related field. 8 years relevant experience or equivalent combination
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soil science. Computational skills including statistical and quantitative analysis. Programming ability in Python. Minimum Qualifications PhD in a related discipline. Minimum of 8 years of relevant work
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like Python and GDAL. Excellent verbal and written communication skills. Minimum Qualifications Bachelor's degree or equivalent advanced learning attained through professional level experience required
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Engineering, Software Engineering, or a closely related discipline. 1 year of experience with multiple programming languages (Python, Java, C++, SQL), deep learning frameworks (TensorFlow, PyTorch), and data
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. The successful candidate should be comfortable using GIS software (e.g., ArcGIS Pro). Familiarity with at least one programming language (e.g., Python, C, C++, Fortran, MATLAB) is advantageous but not