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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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dynamical systems), epidemiological modelling, data analysis (statistics, machine learning). • in scientific programming (preferably Python, Matlab, R) Genuine interest in the analysis and modeling
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language models from LLMs. Demonstrated publication record in the machine learning and AI field. Excellent programming and computer science skills. Preferred Qualification: Doctoral degree in electrical
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language models from LLMs. Demonstrated publication record in the machine learning and AI field. Excellent programming and computer science skills. Preferred Qualification: Doctoral degree in electrical
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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difficult to couple with basin simulators. Geochemical metamodels, particularly those based on machine learning, can significantly reduce computation times while maintaining physico-chemical consistency
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genome-resolved multi-Omics methods, statistical/metabolic modeling, and machine learning. The postdoc will apply these approaches to generate a systems-level understanding of microbiomes including
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allow users to input CDR forcing (e.g., alkalinity addition) and produce day-by-day forecasts of CO2 uptake and storage durability. The project combines physics-based modeling, machine learning, and high
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ML models and Python programming. Work Objectives: The main objective of this position is to develop, implement, and validate advanced machine learning methodologies within the scope of the project
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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