15 machine-learning-modeling-"https:" Postdoctoral research jobs at NEW YORK UNIVERSITY ABU DHABI
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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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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
, and real-time classroom monitoring. Design and implement machine learning models to analyze multimodal data (e.g., student behavior, engagement, and performance) to enhance personalized learning
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, machine-learning model development, structural sensing and health monitoring, conducting physical experiments, and validation of computational models. Required Qualifications: A successful applicant must
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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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in solid mechanics framework Experience in non-linear solid material response and fracture modeling Experience in machine-learning modeling for solid mechanics applications Experience in
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
join forces to push the boundaries of data‑driven discovery. Our mission is two‑fold: to advance fundamental theory in probability and machine learning, and to translate those breakthroughs into high
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in IEEE Communications Society’s and IEEE Signal Processing Society’s journals and conferences. Strong background in communication theory, signal processing, machine learning, and optimization theory
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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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with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running natural language processing and machine learning workflows; (3) experimental design
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or junior graduate students. A formal training, education, or certification in a secondary area (beyond the main PhD focus) such as additive manufacturing, advanced/hybrid manufacturing, machine learning