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Field
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the supervision of Dr. Joseph Kwon. We’re seeking a creative Postdoctoral Scholar to join our team and advance cutting-edge multiscale and hybrid modeling—integrating first-principles simulations with
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Electrodynamics General Circulation Model (TIE-GCM) is a global 3D numerical model that simulates the coupled ionosphere-thermosphere system from ∼97 to ∼600 km height. It self-consistently solves the fully coupled
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identical conditions, using Trichloroethylene (TCE) taken as a model molecule. In addition to physical measures, a methodology based on the analysis, in solution, of TCE and its degradation by-products will
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Models for Multi-Modal Reasoning This project explores the integration of graph-based foundation models (e.g., knowledge graphs) with large language models (LLMs) to build AI systems capable of reasoning
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responsible for conducting numerical and theoretical studies of turbulent flows and their associated geometric structures, focusing on passive and active scalar fronts, multiphase mixing layers, and
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on Svalbard and involve field work activity in Svalbard, during both winter and summer seasons and the implementation of the measured data into numerical models. Travel to the project’s partner institutions
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opportunities. Responsibilities: Write and run evolutionary biogeochemical numerical models to simulate lifeless and inhabited worlds, and develop disequilibrium-, redox-, and information-based metrics
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of Biochemistry, Molecular/Cell Biology, and/or BioEngineering Experience with fluidics devices, 3D printed devices, 3D cell culture models and bioinformatics/computational biology (e.g., R, Python) is an asset
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are recruiting an exceptional post-doctoral researcher to lead technical development in our pioneering project creating the first computational framework for simultaneously modeling Icelandic speech and sign
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understanding of non-stationary complex systems through theoretical analysis and numerical simulation develop efficient statistical algorithms for analyzing and inferring dynamical models from multivariate time