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Field
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Dr. Adam Coates and includes Drs. Carrie Fearer, Verl Emrick, and Mark Ford as Co-PIs. Goals for this project include: (1) Inventory of fuels, using traditional transect methods and terrestrial laser
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-learning methods, knowledge-graph and ontology-based scientific data infrastructures, and agentic workflows for autonomous hypothesis generation, mechanistic exploration, and design of catalytic systems
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computational background. • Proficiency in spike train analysis and neural decoding methods. • Advanced programming skills in MATLAB, Python, or R. • Experience with surgical procedures and in vivo recording
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in the area of probabilistic methods in machine learning. These include applications of Random Matrix theory and Gaussian processes to Deep Neural Nets. Teaching may be required. Education and
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) methods for modeling and optimization of metallic materials and advanced manufacturing processes. Participate in the design of integrated, scalable numerical methods and uncertainty quantification. Follow
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including field, laboratory, and numerical approaches to better understand coastal processes and engineering systems that protect people, property, and infrastructure from storm damage. The successful
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background in Control Systems Theory (e.g., MIMO control, state-space methods, optimal control, model predictive control, system identification) AND/OR Artificial Intelligence / Machine Learning / Data Science
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deep learning approaches, with a particular interest in developing methods capable of handling scarce or corrupted data, designing methods for specific imaging modalities, or understanding and
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related field by the appointment start date. The successful candidate will also have: A strong background in numerical modeling and the application of computational methods to land surface hydrological
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, Psychology, or Indigenous Health, and the ability to travel. Preferred qualifications: Experience with qualitative, quantitative, and/or mixed methods research methods Experience related to research projects