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Field
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infrastructure to capture and curate both experimental and simulation data and collaboratively build machine learning models to predict behavior of novel materials for memristor applications. This is an
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modeling—integrating first-principles simulations with machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model
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modeling—integrating first-principles simulations with machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model
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of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing. Author peer reviewed papers for internal and external release as
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Carilion (VTC). About the Lab: The lab integrates state-of-the-art neural recording technologies, complex cognitive tasks, and computational models to investigate the neural basis of flexible cognition. From
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zonotopes in digital circuits and the model checking of discrete-space systems, with the goal of developing methodologies and tools that make verification scalable and reliable. Your research will focus
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conduct assigned research projects involving human tissue and fluid samples as well as mouse models. Responsibilities include animal tissue harvest and processing, flow cytometry and FACS sorting, neuronal
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are especially encouraged to apply. Extensive experience with protein expression and purification is required. Experience with cryo-EM and model building is desirable but not required. Position and Environment
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
batch adsorption experiments, packed-bed column testing, sorbent regeneration and mathematical modeling of separation systems is also desirable. Create a Job Match for Similar Jobs About The University
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: Topologically-Informed Large Language Models for Innovation Intelligence. The project is funded by FFplus, a EuroHPC Joint Undertaking initiative under the European Union's Digital Europe Programme, and brings