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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 277494287 Position: Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation
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models that include these mechanisms. The postdoc will develop biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM
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, mathematics, computer science, engineering or a related discipline Required Other None Additional Preferred Experience working in one of the following areas: Machine learning/predictive modeling
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None Additional Preferred Experience working in one or more of the following areas: Longitudinal data analysis Predictive modeling/machine learning models Biostatistics / epidemiological modeling
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Associate selected for this position will work as a member of an interdisciplinary team on research involving the use of statistical and machine learning methods for the development of Immune Digital Twins
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, creates tools for synthetic observations, accelerates forward modeling using machine learning, and couples physics-based models with simulation-based inference techniques. Our goal: create a robust
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biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM data and spatial-omics data collected from state-of-the-art