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Field
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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significant impact on the world around us. A postdoctoral position is available in the Department of Electrical and Computer Engineering at Carnegie Mellon University (Chamanzar Lab) on Ultrasound-assisted
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. We are seeking a Postdoctoral Researcher to join the team and make significant contributions to the field. The researcher is expected to have (i) strong machine learning skills to improve model
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, or reinforcement learning. Experience with high-performance computing (HPC). Experience supervising students or junior researchers. What you will do As a postdoctoral researcher, you will: Develop machine-learning
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Post-Doctoral Associate in the Division of Engineering (Mechanical Engineering) - Dr. Mohammed Daqaq
and experimental, in the broad field of nonlinear mechanics. Preference will be given to applicants with expertise in machine learning, wave propagation, metamaterials, and/or fluid–structure
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-quality research results in top-tier venues spanning machine learning, reinforcement learning, embodied AI, and AI in education (e.g., NeurIPS, ICML, ICLR, ACL, CoRL, IEEE ICRA/IROS, AIED, EAAAI
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processing, machine learning, computational imaging, and clinical translation. Beyond your individual research contributions, you will serve as a technical coach for the PhD researchers, helping to align
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conduct world-class applied research. We change and make a difference. Do you want to become one of us? This postdoctoral position is part of the newly funded KKS Synergy project WorkFlex+ which focuses
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Missouri University of Science and Technology | Rolla, Missouri | United States | about 2 months ago
position focused on advancing crop production and physiology through field experimentation, computation (statistics and machine learning), and process-based models. The successful candidate will have an
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cardiovascular care. Within the consortium, TU Delft contributes expertise in cardiac mechanics, soft tissue modeling, growth and remodeling, machine learning, and uncertainty-aware model personalization. As a