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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 15 hours ago
Computational Medicine Program at the University of North Carolina – Chapel Hill (http://miaolab.org ) invites applications for a Postdoctoral position in AI driven drug discovery and accelerated molecular
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micro and nano-technologies (characterization, simulation, modeling), as well as in the design and characterization of circuits, microsystems and systems. Located at the convergence of many sciences and
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theoretical models and numerical tools (master equations, quantum trajectory simulations) to investigate coupling regimes, dynamical phase transitions, and the effects of collective dissipation on coherence and
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characterize their electromagnetic performance. To validate the achieved tuning range and functional performance in laboratory measurements against modeling and simulation results. This position is part of
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the development and refinement of Monte Carlo simulation generators to accurately model neutrino interactions with various target materials. Detailed comparisons of these simulations to data from existing neutrino
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analysis of spatial-omics and imaging data, developing a framework for simulations, simulating epidemics on social networks, building generative models to improve computation time for simulations, and other
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, the postdoctoral researcher will be responsible for contributing to the development of advanced methodologies for predicting crystal structures (CSP) based solely on their chemical composition and atomistic modeling
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, including parameter optimisation and advanced monitoring techniques. Develop and refine process models, simulations, and automation strategies. Characterise microstructure, wear behaviour, and corrosion
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-the-loop (HIL) validation. The Research Staff will contribute to the design, modelling, simulation, validation, and optimization of electric vessel power systems, with strong emphasis on battery-based
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equations (SDEs) are widely used to model time-evolving phenomena in finance, biology, and social dynamics, the underlying data is often highly sensitive. This project tackles the intersection of complex