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Field
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Learning for Biomedical Data. The postholders will focus on developing and applying state-of-the-art generative models (such as VAEs, GANs, and transformer-based architectures) to large-scale biomedical
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modeling, control, and optimization of power and energy systems with applications to maritime and coastal infrastructures (e.g., shipboard microgrids, port facilities, islanded communities, desalination
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big data and AI at its core. A central goal of the project is to build a foundation model of the visual brain—a “digital twin” that captures neural activity and intelligent behavior at unprecedented
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the atmosphere and understand transport pathways by combining observations of PFAS in precipitation from the National Atmospheric Deposition Program with atmospheric modeling techniques. This position primarily
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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Include: Design and implementation of computational cognitive models Capture workflows and strategies in complex software engineering systems in their integration in real-time recommender systems Carry out
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. The successful candidate will be a member of a highly interdisciplinary team including oncologists, biologists, engineers, and imaging scientists. The candidate will develop computational models of human disease
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, execution, and data interpretation) Numerical modeling and simulation to complement physical experiments Preferred) Proficiency in CAD and modeling tools such as SolidWorks for experimental design and
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will collaborate with faculty, research staff, and students. Depending on candidate interest, topics of study may include: Prompts as a data primitive and related methods Foundation model adoption and
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to detector/system modeling and optimization for count rate, resolution, and throughput. Document methods and develop user-facing procedures and best practices for reliable operation during user runs