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Qualifications The following qualifications and experience will be considered an advantage: Experience with crop modeling. Experience with plant breeding. Background in data science, machine learning, and
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aims to explore to which extent machine learning methods can help with these tasks, e.g. object reconstruction and signal/background discrimination. This will be a focus in the project. One exciting
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for building energy-efficient and robust brain-inspired, autonomous, and cognitive systems and intelligent vision systems, including efficient learning and inference of complex AI/ML algorithms, specialized
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, and artificial intelligence methods. Assess drought stress and identify physiological traits associated with drought tolerance using advanced imaging technologies. Develop machine learning and deep
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populations. Apply Artificial Intelligence (AI) methods including deep learning (DL) models and supervised and unsupervised machine learning (ML) methods for integration and for Genome-2-Phenome (G2P) and risk
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candidates whose expertise falls within one or more of the following areas: computational and mathematical modeling, statistical modeling, machine learning, network science, bioinformatics, applied mathematics
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. The position requires experience with at least one of the following: Data Science, Machine Learning, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data management
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and intelligent vision systems, including efficient learning and inference of complex AI/ML algorithms, specialized neural processing hardware and design tools, and ML security, and their applications
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State University and work closely with I-CREWS researchers across the state. The position emphasizes developing, integrating, and applying modeling approaches-including machine learning (ML), hydrologic
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-XRF, Raman, FTIR in reflection mode) to enable multimodal data fusion and automated material characterization. • Apply and further develop machine-learning and statistical models (e.g. PCA, SAM