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research in several areas. These include, but are not limited to: Exploring machine learning techniques to analyze current systems and assess opportunities for improvement Gaining experience with virtual
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decision-making by forest managers, planners, and policy makers. This project will inform the Forest Service’s Resources Planning Act (RPA) Assessment. Learning Objectives: The participant will have the
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an excellent opportunity for someone eager to grow their skills in clinical research with an emphasis on novel technology development. Why should I apply? Under the guidance of a mentor, you will learn
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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
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of hydrate distributions and fluid migration in porous media under in situ conditions, and • Machine learning application to gas hydrate system to develop efficient key parameter estimation tools and large
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resolution visualizations of hydrate distributions and fluid migration in porous media under in situ conditions, and • Machine learning application to gas hydrate system to develop efficient key parameter
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and weaknesses for end-users. Help develop new or improve existing soil moisture estimates using NISAR and other datasets utilizing artificial intelligence (AI) and machine learning. The outcome from
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and expected carbon remaining. The successful applicant will collaborate closely with research scientists and analysts at the Missoula Fire Sciences Lab on this project. The participant will learn
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& Amputation Center of Excellence (EACE) is a unique organization within the Department of War (DoW) consisting of teams of researchers embedded at the point of care within multiple Military Treatment Facilities
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and binding mechanisms for the lithium observed in formation and related geologic formations. The learning objectives for this project are: · Conduct research about the lithium isotopes (d7Li