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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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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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) 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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(e.g., finite element or wave propagation simulations) for defect detection and materials analysis Integrate AI, machine learning, and robotics into NDE and manufacturing processes for automation and
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high intellectual merit and the willingness to use machine learning and/or AI techniques. Essential Duties and Responsibilities: Successful candidates are expected to develop an impactful research
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physics. Development of new artificial-intelligence and machine-learning techniques for high-energy and nuclear physics. Close interaction with our collaborators in the EIC and the BNL theory group will be
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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