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, GIS, or environmental exposure modeling. Demonstrated experience with Bayesian modeling, simulation methods, machine learning, or uncertainty analysis. Demonstrated leadership in research design
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hydrological and related datasets • Apply machine learning and AI techniques to interpret water system dynamics • Develop and implement algorithms using MATLAB and other programming languages (e.g
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modeling outputs into operational decision-making systems. Develop Advanced ML/AI Models for Air Quality Applications Applies machine learning (ML) and artificial intelligence (AI) techniques to enhance
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not required, we are particularly interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and functional
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coordinating undergraduate courses in data and analytics, such as Business Statistics, Management Science, Data Mining, Machine Learning, Predictive Analytics, Advanced Spreadsheet Modeling, and
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interest in time-series modeling and physics-guided machine learning. Proficiency in Python and modern deep learning frameworks (e.g., PyTorch). Strong engineering maturity, including experience with
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developing novel alloys for energy applications in harsh environments using additive manufacturing. This research involves integrating computational modeling, machine learning, and experimental
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collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL ( Prof. Olga Fink ). IMOS develops advanced machine-learning and AI methods for complex engineering and
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qualifications: Strong background within machine learning/deep learning, and immunoinformatics is a requirement Knowledge of structural biology and protein structure modeling is a plus Knowledge of the basic
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Join us in designing stable materials for sustainable energy devices with machine-learning-accelerated simulation and modeling. Work assignments The postdoctoral researcher will develop machine