40 python "Computer Vision Center" Postdoctoral positions at Michigan State University
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methods to biological systems Proficiency in Python programming Excellent written and verbal communication skills First-author peer-reviewed publication(s) Desired Qualifications Experience
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ultrasound, wearable and accelerometry-derived loading or physical activity analytics, gait biomechanics, or circulating biomarker analysis. Programming proficiency in R, Python, or MATLAB. Experience with
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lake biota and harmful algal blooms at the continental scale (funded by the National Science Foundation). This postdoc will be at the center of a new initiative turning 51 years of Michigan lake and
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Minimum Requirements 1. A PhD in Mechanical, Civil, Materials, Aerospace Engineering (focus on solid mechanics, dynamics / vibrations). 2. Proficiency in Matlab and/or Python. 3. Experience in Finite
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troubleshooting. Strong Python skills are required for workflow automation, data processing, testing, and model orchestration. Familiarity with C or C++, APIs, dynamic-link libraries, version control, Linux
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Python and R programming for statistical modeling, data visualization, and integration of large-scale genomics datasets Participate in the design of strategic data acquisition plans to support ongoing
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, data analysis, and visualization environments/IDEs (e.g., R/R Studio, Python/Spyder/VSCode) Familiarity with GIS methods and cloud-based computing resources (e.g., Google Earth Engine) for large-scale
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with the Quello Center for Media and Information Policy in the Department of Media and Information and the Future of Work Initiative in the College of Social Sciences at Michigan State University. The
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integrating geophysical data with groundwater or vadose zone models Familiarity with irrigation systems and agricultural water management Programming experience (e.g., Python, MATLAB, R) for data analysis and
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with bioinformatic analysis of high-throughput sequencing data Proficiency in R, Python, or other relevant computational tools Evidence of scholarly productivity (peer-reviewed publications) Excellent