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methods and software for the analysis and calibration using very large data sets resulting from dynamical simulations on high-performance computing resources. Application areas include : 1. Epidemiology, 2
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contributions in: Building novel generative models for predicting genome-scale evolutionary patterns using GenSLMs Developing scalable models that can, when integrated with high throughput molecular dynamics
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of molecular reactions occurring at the surface of various materials. In addition, computational fluid dynamics (CFD) simulations combined with microkinetic modeling will be carried out to study the heat
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Software-Defined Networking (SDN) solutions to dynamically manage network congestion and improve communication efficiency. Research and develop topology-aware collective communication algorithms to optimize
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all areas of experimental condensed matter physics will be considered, particular emphasis will be placed on the dynamical studies of 2D materials and their functionalities. This postdoctoral position
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis
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multidisciplinary team comprised of fellow postdoctoral appointees, experimentalists, and staff scientists, with computational fluid dynamics (CFD) and artificial intelligence/machine learning (AI/ML) expertise, with
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familiarity with DMS and DERMS functionalities and architectures; evaluate and enhance operational schemes under high DER penetration. Model microgrids in distribution networks including dynamic and steady
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approach with the ability to work independently to deliver. Skills in written and verbal communication, with the ability to present complex information in a clear and concise manner. Knowledge in energy