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• Skilled in single-cell/population data analysis (e.g., GLMs, decoding) Preferred Qualifications • Background in machine learning or computational modeling (Bayesian methods, neural networks, etc
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on the development of Bayesian statistical/machine learning methods for the data integration analysis of high-throughput imaging and molecular data (i.e., genome, transcriptome, epigenome, and more). The methods would
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Institute (https://cse.umn.edu/aiclimate). The role involves building knowledge-guided machine learning (KGML) models for sustainable agricultural practices, developing AI-ready benchmark datasets, and
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Postdoctoral Research Associates to support multidisciplinary research on (1) Performance prediction of large-scale concrete dams using machine learning techniques, 2) Fiber-optic sensor-based monitoring
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the results (e.g. worksheet, graphs, tables, etc.) and assists in developing appropriate computer programs. o Analysis of data obtained as a result of experiments performed, and preparation of laboratory
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status, sexual orientation, gender identity, or gender expression. To learn more about diversity at the U: http://diversity.umn.edu Employment Requirements Any offer of employment is contingent upon
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artificial intelligence (AI) and machine learning (ML) methodologies and interested in advancing these tools for accelerating the analysis of the big data acquired by electron microscopy. • You work
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extended to its full duration. Preferred Qualifications Experimental skills in batteries and power electronics, as demonstrated by application materials. Knowledge of machine learning, as demonstrated by
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materials. * You have familiarity with artificial intelligence (AI) and machine learning (ML) methodologies and interested in advancing these tools for accelerating the analysis of the big data acquired by
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, enhanced by machine-learning and data-driven analysis techniques. Additionally, the study will encompass electrically triggered events that mimic the voltage-based signaling of biological synapses