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Requisition Id 16930 Overview: We are seeking a Postdoctoral Research Associate to advance the scientific validation of quantum simulations for quantum materials. This work directly supports the
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Requisition Id 16802 Overview: We are seeking a Postdoctoral Research Associate for the development and application of advanced multiphysics simulations, and machine learning (ML) methods relevant
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Ridge National Laboratory (ORNL) seeks a motivated Postdoctoral Research Associate. This position primarily focuses on large-scale molecular dynamics (MD) simulations and AI-integrated multiscale modeling
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
that can incorporate multi-scale computational simulations to aid with data fusion across multiple modalities of experiments with the final goal of discovering novel materials phenomena or even new materials
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generating fusion energy. This research will focus on the chemical speciation and transport of tritium in the molten salt blankets using ab initio quantum simulations, machine learning potentials, and
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, qualification, and deployment of AI agents and models, Computational Fluid Dynamics (CFD) simulation codes, and Finite Element Method (FEM) based tools for nuclear energy (fission and fusion) applications
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Requisition Id 17070 Overview: We are seeking a Postdoctoral Research Associate who will use multiscale modeling and simulation to develop probabilistic lifting frameworks for high-temperature
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). The successful candidate will contribute to the modeling, simulation, and co-design of next-generation Quantum-HPC (QHPC) architectures, with particular emphasis on integration of quantum and HPC distributed
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into closed-loop control frameworks Test and validate control algorithms through simulation, hardware-in-the-loop testing, and/or physical testbeds Apply machine learning techniques to predict thermal system
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structure, and simulation of coupled difference or differential equations. Experience calibrating simulation models against sparse, indirect, aggregated, or otherwise limited observations. Familiarity with