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Post-Doctoral Associate in the Center for Translational Medical Devices (Modeling) - Dr. Rafael Song
establishing collaborations across disciplines. Applicant must have a PhD in an engineering or biomedical-related field. Excellent skills relevant to the computational simulation of medical devices (3D solid
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establishing collaborations across disciplines. Applicants must have a PhD in an engineering or biomedical-related field. Excellent experimental skills relevant to the project (computational simulations, device
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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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consisting of PhD-level scientists, graduate students and undergraduate students and across multiple institutions (Khalifa University, Cornell University). Applicants must have a PhD in Bioengineering or
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, analyzing the data on dynamics in individual behaviors. Successful applicants must have a PhD in computer science, digital technology, mathematics, or related fields with demonstrated experience in developing
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experimental data. Required Qualifications: A successful applicant must have a PhD in Engineering Mechanics, Civil Engineering, or Mechanical Engineering. Applicants are expected to demonstrate research
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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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measurements for RIS enabled communication environments, as well as processing and analyzing the collected data. Applicants must hold a PhD degree in Electrical/Electronics Engineering, telecommunications
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monitoring, conducting physical experiments, and validation of computational models. Required Qualifications: A successful applicant must have a PhD in Civil Engineering, Engineering Mechanics, or Mechanical
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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