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Post-Doctoral Associate in the Center for Translational Medical Devices (Modeling) - Dr. Rafael Song
, cardiovascular, and neurologic disease. These projects entail computational modeling, device design and manufacturing, optimization of chemical, mechanical, and electrical characteristics, and preclinical
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-edge machine learning, including Large Language Models (LLMs), to enhance decision-making and planning in robotic systems. Qualifications: Applicants must have a PhD in Robotics, Control Theory
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structural centrifuge testing and physical modeling. Advanced instrumentation and experimental simulation. Successful candidates must hold a PhD degree in Civil, Structural, Mechanical, or Material Engineering
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Interdisciplinary Collaboration Program in a project that spans oceanography, ecology and mathematics. The position will be based at NYU Abu Dhabi starting September 1, 2025 for two years, renewable depending
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narratives in behavior and psychological adjustment. Successful candidates are expected to develop a productive and collaborative research program addressing questions related to how early experiences with
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, cardiovascular, and neurologic disease. These projects entail computational modeling, device design and manufacturing, optimization of chemical, mechanical, and electrical characteristics, and preclinical
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, cardiovascular, and neurologic disease. These projects entail computational modeling, device design and manufacturing, optimization of chemical, mechanical, and electrical characteristics, and preclinical
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or Python) is required. Experience with large-scale concrete testing, modeling, and simulation (e.g., COMSOL, Abaqus) is a plus. Familiarity with AC impedance spectroscopy, electro-mechanical impedance, wave
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trajectories and enhance interaction safety and robustness. Learning for Human-Robot Interaction: Integrate machine learning techniques, such as reinforcement learning and generative models, with control theory
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power (CSP) systems optimization, computational heat transfer and radiative transport using sophisticated numerical modeling and machine learning approaches for forward and inverse problems in radiation