20 parallel-programming-"Inria" Postdoctoral positions at Texas A&M University System
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. Qualifications: Required Education Appropriate PhD Preferred Qualifications PhD, Computational Biology, Bioinformatics, Computer Science Training in statistics, and programming experience in a UNIX/Linux
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, surveys, etc.), research program management, and reporting protocols Conducts collection and analysis of data, particularly utilizing Qualtrics and or RedCap survey software/programming and statistical
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develop measurable project goals and objectives and monitor progress toward achievement. Designs and delivers student workshops and programming events for rural incoming and current college students
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, mechatronics, or controls researcher with demonstrated experience in automation, system integration, and PLC programming. Experience with AI/ML-based control is a strong plus. No prior background in
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. • Ability to multi-task and work cooperatively with others. Preferred Qualifications: • Proficiency in handling remotely sensed geospatial datasets and programming languages, particularly R or Python
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relevant field (Horticultural Sciences, Plant Genetics, Genomics, Breeding, or closely related field). Required Knowledge, Skills, and Abilities: Experience in R programming and software for genetic
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Proficiency in numerical analysis using finite element method (FEM) software (e.g., COMSOL, ANSYS, or custom FEM codes) Proficiency in one or more programming languages (e.g., MATLAB, Python, C++, Julia
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programming languages (R or Python) for automated reproducible workflows. • Knowledge, experience, and interest in assessing the impacts of management practices on environmental outcomes, such as soil
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experimental trials. Extract feeding behavior data, including bunk visits, feeding rate, and feeding duration. Conduct statistical data analysis using current software and develop programming to
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. Strong computational skills, including programming experience in Python, R, MATLAB, C/C++, or similar languages. Familiarity with artificial intelligence/machine learning and spatial and temporal data