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About Us We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with experience in: Deep learning Medical imaging computing
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, focusing on high-performance deep learning for neural implicit reconstruction of ultrasound data. The goal is to advance the scalability and efficiency of neural radiance fields (NeRFs) and related
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Abrahao (NYU Shanghai) and João Sedoc (NYU Stern). Research Focus Areas Our research encompasses topics in DL and AI, including but not limited to: Deep Learning Algorithms and Paradigms Generative Models
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qualifications, the need to acquire experience, previous experience, and previous satisfactory employment under the provisions of this collective agreement. Application Procedure: For detailed information
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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Responsibilities: To conduct independent research in deep learning and large language models. To produce publications in top conferences and journals. To offer guidance and assistance to any students involved in
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-campus for the Fall 2025 Semester (09/02/2025 - 12/14/2025) Course: Deep Learning This course covers the theory and practice of deep learning, including neural networks, convolutional networks, and
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the internationally renowned INTERGROWTH-21st Consortium. This role involves developing advanced deep learning techniques to identify early indicators of fetal vulnerability using clinically-acquired ultrasound (US
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deep reinforcement learning (DRL) methodologies for both gene-level and edge-level perturbation control, represents a significant advancement in the computational toolkit for cellular reprogramming