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. The project will also delve into generation and integration of synthetic data, via deployment of numerical simulations with existing advanced calibrated multiphysics models, with real data acquired through
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the limitations of current simulation methods by enhancing machine learning interatomic potentials and develop ML approaches to model rare events and complex environments. A key focus will be solving the "chicken
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on numerical simulation of the turbulent urban boundary layer using the transient mesoscale model PALM‑4U. The work involves developing the model to integrate multi‑physical parameters and boundary conditions
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focus on developing a molecular- and system-level understanding of CO₂ capture and release in e-DAC systems using computational modeling and simulation. The PhD candidate will apply computational
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understanding of molecular thermodynamics, and realize the importance of different types of properties in selecting and developing the most physically sound thermodynamic model for water and electrolytes
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on reinforcement learning (RL) for policy discovery in a multi-sector “integrated modeling environment” that connects fast ML metamodels of simulators (e.g., transport, energy, environment, climate events). The aim