51 modelling-and-simulation-of-combustion-postdoc Postdoctoral positions at Princeton University
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
for this position will work to develop a conservative machine-learning based sea ice model correction that can be applied to fully coupled climate model simulations. The project will involve: 1) the development of a
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group's efforts in modeling combustion-generated aerosols. These modeling framework will be used to understand the impact of inorganic aerosols on sunlight scattering and droplet/ice crystal nucleation
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
increments, which represent structural model errors (https://doi.org/10.1029/2023MS003757). When applied online to global ice-ocean simulations, this neural network substantially improves sea ice simulation
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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Values. The teaching will likely involve running a senior thesis seminar rather than teaching a traditional course, subject to approval of Princeton's Office of the Dean of the Faculty. The postdoc will
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maintaining a shock tube facility (operational proficiency required)Kinetic modeling proficiency (Chemkin, Cantera), analytical proficiency (sensitivity, rate of production, etc.)Spectroscopic modeling
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simulations, statistical mechanics, computer programming (e.g., C++, Python), polymer theory, molecular modeling (e.g., of proteins, nucleic acids, ligands), coarse-grain and polymer model development
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required) Kinetic modeling proficiency (Chemkin, Cantera), analytical proficiency (sensitivity, rate of production, etc.) Spectroscopic modeling experience preferred (HITRAN/HITEMP) Familiarity with
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials