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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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boson decays, searches for supersymmetry and other new phenomena, and measurements of rare standard model processes. We vigorously pursue the use of machine learning techniques for data analysis
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germplasm characterization, including calculation of sequence based genomic prediction and genomic selection values. To learn more about the USDA ARS NPGS and Plant Genetic Resources, visit our webpages
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decisions. Adheres to University and unit-level policies and procedures and safeguards University assets. Develop various machine learning and data mining models including deep convolutional neural networks
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, integrates, and evaluates innovative solutions in autonomous systems, including artificial intelligence (AI), machine learning, robotics, control systems, sensing technologies, and related disciplines
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Requisition Id 16723 Overview: We are seeking a Postdoctoral Research Associate with expertise in artificial intelligence (AI) and machine learning (ML) for multiscale physical systems
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, implement and benchmark machine learning models for large-scale health datasets consisting of diverse information including structured medical history, demographics, clinical notes, laboratory measurements
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date). Demonstrated research experience with quantitative ultrasound methods. Strong background in model‑based or physics‑informed machine learning, with experience developing algorithms that integrate
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: A doctorate in a Machine-Learning related field A deep knowledge of Control Theory, both classical and deep learning based A solid publication record in top level ML venues such as NeurIPs, ICML, and
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design/modeling/integration, and active filtering. ● Converter control/modeling, including and not limited to high-speed motor drive and grid-tied converters. ● High voltage engineering and