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Field
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, and energy efficiency—and meeting these demands will require smart, distributed computing built directly into the network. This research project focuses on designing AI-native edge computing systems
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the distribution of diverse gene classes to the ecology of fungi [1] and predicted the presence of traits that were considered absent from fungi [2]. Currently, only homologs of defined and relatively conserved
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focus often neglects other dimensions of processes, such as the time between activities and the time required for specific steps, the spatial distribution of tasks across locations, or the intricate
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: Professor Zhongwen Zhan) to design, build, and validate a miniaturized Distributed Acoustic Sensing (DAS) system for the lunar environment. Key responsibilities: - Architect and prototype a low-SWaP (Size
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trigger reconstruction architectures for future particle collider experiments, based on deep learning models distributed across multiple hardware processing stages. The mission of this position, based
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the U.S. achieve energy goals. ESIA develops, deploy, and advances grid technologies that ensures a robust and secure U.S. grid transmission and distribution system. ESIA also collaborates with government
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Your Job: Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids Apply machine learning/AI or surrogate modeling (e.g., neural networks
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ecosystem physiological measurements from ground based eddy covariance towers, and modification of existing model structure and development of novel algorithms to combine in situ ecosystem physiology
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generate, transmit, and detect OAM-entangled photons under realistic atmospheric turbulence. Deep learning algorithms will be employed to pre-compensate distortions in real time, maximizing state fidelity
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for improving the simulation and optimization of distributed systems, for instance by specializing neural ODEs and their training routines. This research will address challenges driven by the energy transition