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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 271598471 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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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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scientific machine learning in solving problems in solid mechanics and dynamic wave propagation, in particular: (i) developing domain decomposition methods, (ii) damage models, (iii) nonlinear mechanics. 2
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groups led primarily by Professor Tarek Abdoun and Professor Mostafa Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development
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groups led primarily by Professor Tarek Abdoun and Professor Mostafa Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development
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implement computational models and perform deep learning analyses for the early detection of Alzheimer’s disease using MRI, biomarkers, and advanced computational techniques · Collaborate with a
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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studies. Develop and apply advanced statistical methods and machine learning techniques using tools such as R and Python. Integrate and run process-based models (e.g., crop models, hydrologic models
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for interacting with colleagues and stakeholders. Department Specifics: Develop various machine learning and data mining models including convolutional neural networks (CNNs), Transformers, large language models