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, immunofluorescence. • Experience analyzing, graphing and interpreting research results • Experience with oral and written communication of scientific results • Mentoring and leadership potential • Familiarly with
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/PyTorch for deep learning), possibly theory development, simulation studies, real data analysis, and writing manuscripts. Duration: This appointment is for 1 year, may be extended for a second and/or third
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modeling. 80% research - The project focuses on developing theoretical models using optimization and information theory to improve understanding of plant hydraulic regulation at the leaf, plant, and
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software development (mostly in R, or in Python/TensorFlow/Keras/PyTorch for deep learning), possibly theory development, simulation studies, real data analysis, and writing manuscripts. Starting Date