98 image-processing "Integreat Norwegian Centre for Knowledge driven Machine Learning" Fellowship positions at Zintellect
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in applying process-based cropping system models to quantify the short- and long-term effects of these conservation practices on soil health and crop productivity for U.S. growers. This specific
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sensing and decision-making in poultry processing environments. Learning Objectives: Under the guidance of a mentor, the participant will gain knowledge and experience in: Applying hyperspectral imaging
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. This fellowship offers a unique opportunity to be at the forefront of groundwater technology, gaining experience on projects ranging from groundwater sampling and geophysical imaging in the field, analysis
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therapeutics. The experience includes opportunities to learn the operation and upkeep of physiological and cell culture instrumentation, apply quality assurance and quality control practices, and contribute
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include the development of predictive models for disease resistance, genome-wide association studies to uncover resistance loci, automated phenotyping approaches using image data, and integrative multi
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concept to prototype testing and calibration. This solid engineering background will be advantageous to the participant in their future career. Mentor(s): The mentor for this opportunity is Samir Trabelsi
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-automated processing pipeline capable of analyzing high-throughput plant phenotyping and soil-sensing data to extract key phenotypic traits. Advancing crop productivity within sustainable cropping systems
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learning about the scientific process and field sampling methods. The fellow will also learn to identify common plant species of the interior Pacific Northwest, collect and process increment cores for tree
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excellence of our agriculture. The vision of the agency is to provide global leadership in agricultural discoveries through scientific excellence. Research Project: The Healthy Processed Foods Research (HPFR
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cannot be feasibly built across the wide range of environmental conditions present in U.S. croplands. To address this, computer simulations can evaluate the performance of different cropping systems across