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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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for physical systems. The postdoc will work on projects focusing on one or more of the following: Robot Learning & Autonomy – Developing algorithms that allow robots to learn (via exploration or imitation) from
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, coding pipelines for optical imaging/photometric data analysis, and spectral modelling, are strongly encouraged to apply. Successful candidates will be encouraged to develop independent projects in
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collaboration between multiple research groups led primarily by Professor Tarek Abdoun and Professor Mostafa Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling
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Reality to elicit users’ preferences for innovative transport systems. Applicants with a background in behavioral analysis and mathematical modelling are encouraged to apply. Terms of employment include
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the safety and robustness of human-robot interaction. The postdoc will work on projects focusing on one or more of the following: Learning-Based Control: Developing reinforcement learning, imitation learning
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Description The Center for Artificial Intelligence and Robotics (CAIR) at New York University Abu Dhabi invites applicants to apply for the open Postdoc position in the Project Collaborative Multi
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modeling and characterization for communication and sensing in emerging spectrum for 6G and beyond, with a focus on the FR3, THz and optical frequency bands. This research will be conducted under the joint
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molecules tailored for DNP-NMR applications, benefiting from NYUAD’s state-of-the-art research infrastructure for organic synthesis and molecular characterization. Key Responsibilities Candidate will be
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, climate, and human health. Examples of current active projects include: Developing optimization models to analyze and mitigate fine particulate matter (PM2.5) exposure from various infrastructure systems