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ecological modeling. Learning Objectives: Through this fellowship, the successful applicant will gain valuable hands-on experience and develop expertise in laboratory and cold chain research. The participant
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areas. These include, but are not limited to: Applying machine learning algorithms to solve real-world problems. Creating and structuring databases for storage, retrieval, and image analysis. Determining
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levels at harvest, we aim to develop predictive models, powered by deep neural networks, that can detect early signs of fungal infection and evaluate mitigation strategies such as soil amendments
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. Learning Objectives: The participant will learn skills in the Protein Function and Phenotype Prediction SCINet group. They will learn to develop and deploy AI-based protein structure prediction tools
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flood-induced resistance. Learning Objectives: The participant will expand their skills in plant metabolite analysis, plant genetics, and plant stress biology. They will also receive mentoring in writing
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Organization DEVCOM Army Research Laboratory Reference Code ARL-C-CISD-300144 Description About the Research Current approaches optimize machine learning training largely by exploiting Deep Neural
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will develop research treatments and fully collaborate on research during the course of their postdoc program. The postdoc will statistically analyze data with the guidance of his/her mentor. Learning
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Organization National Energy Technology Laboratory (NETL) Reference Code NETL-Postdoc-2026-Seol How to Apply A complete application consists of: An application, including academic history, work
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electrical penetration graph (EPG) technology. The candidate will perform laboratory, greenhouse, and field studies, and to establish experiments to test different pest management strategies. Learning
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learn how phenotypic datasets are integrated with genomic data for association analyses, genomic selection, and AI-driven methods, including machine learning and deep learning, to enhance germplasm