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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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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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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 2 months ago
, submesoscale dynamics, and data analysis techniques, complementing ongoing efforts in developing mathematical and machine learning methods to inferupper-ocean transport from SWOT; and tackle challenges involved
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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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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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, age, marital status, disability, public assistance status, veteran status, sexual orientation, gender identity, or gender expression. To learn more about diversity at the U: http://diversity.umn.edu
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for 2 years. You will lead a research team working on the application of software (AI/machine learning techniques), to improve the efficiency of advanced hardware and integrated circuit design. This role
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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