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Field
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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approaches and will integrate novel hardware (including electrode arrays, microdevices, analytical systems) into automated robotic pipelines You will also apply machine learning-based analyses to imaging and
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of the extraction and beneficiation system. This work will require an understanding of mining processes, mathematical modeling of flows and extraction decisions, and the use of machine learning algorithms to predict
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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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field. Strong background in machine learning, particularly deep learning and natural language processing. Experience with transformer-based architectures (e.g., BERT, GPT) is highly desirable. Proficiency
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values across different omics layers and platforms. Cross-omics data fusion and representation learning for comprehensive systems biology modeling. Identification of causal relationships and biomarker
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background in machine learning, predictive modeling, or applied AI Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow. -Experience working with real-world datasets
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: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics
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. This unique position combines advanced finite element modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an
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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles