24 modeling-and-simulation-post-doc Postdoctoral positions at Oak Ridge National Laboratory
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of irradiated ceramics and alloys for tritium technology development using advanced experimental and computational methods. The researcher will perform characterization of model systems using techniques such as
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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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well as dynamic and transient inverter modeling and different applications of the simulation. Selection will be based on qualifications, relevant experience, skills, and education. You should be highly self
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Requisition Id 16715 Overview: We are seeking a Postdoctoral Research Associate who will use multiscale modeling and simulation to develop probabilistic lifing frameworks for high-temperature alloys
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modeling, optimal power flow (OPF), surrogate modeling, and data-driven analysis of large-scale electric power system simulations on DOE leadership-class computing resources. The candidate is expected
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or model parallel training. Experience with multi-physics simulations on HPC and with ML models. Experience working in a multi-disciplinary research environment. Demonstrated written and oral
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transfer. Preferred Qualifications: Experience with building energy modeling and simulation tools such as EnergyPlus, or similar platforms for energy simulation and analysis, including model development
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manufacturing datasets, including sensor streams, in-process signals, post-process characterization data, simulation outputs, and digital twin data. Develop, integrate, and evaluate AI/ML models for anomaly
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: Familiarity with architectural simulators (e.g., Gem5, SST) or HDLs. Quantum / Analog Computing: Exposure to quantum programming models (e.g., QIR, Q#, Qiskit, Cirq) or analog/neuromorphic systems; interest in
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, LeafWeb, Sapfluxnet, PSInet) to translate trait variation into model parameter priors and functional constraints, and to explore parameter relationships with environmental conditions Hybrid modeling