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of substitution models using large dataset, successful applicants must then have a PhD and demonstrated experience in discrete choice models, machine learning techniques, big data, and optimization
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
Geotechnical Engineering, Civil Engineering, or a related field, and should demonstrate strong expertise in at least two of the following areas: Large-deformation numerical modeling (e.g., Coupled Eulerian
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of large-scale survey data, which are combined with administrative and experimental datasets to answer high-impact policy and academic questions. Applicants must have a Bachelor's degree in Computer
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tackle a broad spectrum of topics, including pandemic modeling and forecasting, pathogen genomics, epidemiological big-data analytics, global health informatics, and vaccine development strategies. Drawing
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Description Associate Professor - Computer and Information Engineering Job Purpose: The responsibilities of faculty members shall be an appropriate combination of the following: a) Dissemination of
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focuses on the rigorous statistical and probabilistic foundations of machine learning and data science. We emphasize computational methods for large-scale data and scalable inference techniques. Our current
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placement efforts Foster service by contributing to the community at large in participatory, developmental or advisory capacity Support and participate in student organization activities Adhere
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-edge machine learning, including Large Language Models (LLMs), to enhance decision-making and planning in robotic systems. Qualifications: Applicants must have a PhD in Robotics, Control Theory
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development (R&D) of advanced machine learning (ML) models like Transformers, Vision Transformers, Large Language Models (LLMs) and other advanced deep learning models for vision-based applications. The focus
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the eye and brain that explain vision loss, building on our previously-developed method linking clinical, neural and behavioral data (Allen et al., 2018; Miller et al., 2019; Pedersini et al., 2023). We