56 modelling-and-simulation-of-combustion-postdoc Postdoctoral research jobs at Princeton University
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associate positions. Dr. Wei Peng's group (www.weipengenergy.com) focuses on modeling institutional and human dimensions of energy transition to identify realistic and robust decarbonization strategies
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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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to lead an investigation exploring the ability of recently developed global earth system models to simulate coastal sea level across sub-annual timescales. This work will leverage a suite of coupled models
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), seeks a postdoctoral or more senior research scientist to lead an investigation exploring the ability of recently developed global earth system models to simulate coastal sea level across sub-annual
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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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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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at Princeton University.We welcome applications from all areas in mechanical and aerospace engineering, including but not limited to the fields of: Bioengineering Combustion and Energy Science Computational
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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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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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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