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, mobility and cost. This project aims at developing a dehydration monitoring system that fuses multiple modalities of measurement in order to enhance the quality of measurement, and improves usability by
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project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research
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project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research
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road conditions. Your specific activities will include (but are not limited to): • Develop robust, production-grade machine learning solutions for predictive modelling and complex decision
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to pregnancy), sexual orientation, gender identity, gender expression, ethnicity or national origin, religion, age, genetic information, disability, or veteran or military status by any member of the KSU
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computer applications used for data recording, analysis, and reporting. Physical Demands and Working Conditions Physical Activities Working Conditions Additional Information Remote Work: A hybrid remote work
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actively contribute to the WeForming and EnerTEF projects. WeForming and EnerTEF propose developing automatized and intelligent solution for operating active distributed grids with multiple active asset6s
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the project. Strong research profile in the applications of machine learning, artificial intelligence, multi-objective optimization, spatiotemporal modeling, and processing of satellite and high-frequency flux
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specified timelines. Ability to prioritize tasks, set milestones, and monitor progress. Continuous Learning and improvement: Willingness to stay updated with the latest advancements in road condition research
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD (or foreign equivalent) in Biomedical Engineering, Medical Physics, Electrical, Computer Engineering or a