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flow behavior. The project also involves applying machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid
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logistics systems through methodologies of dynamic and predictive optimization, behavioral modeling and machine learning. There is vivid interaction within the group to foster collaboration both with
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experimental design. Collaborate with another postdoc in the NIH Center to use scientific machine learning (SciML) to automatically select mathematical models from data. Minimum Requirements: Ph.D. in applied
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degree in Computer Science, Artificial Intelligence, Data Science, or related field; Solid background in machine learning, deep learning and foundation models such as Large Language Models; Strong
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Engineering). - Theoretical foundations of 6G RAN and autonomous systems o Proven knowledge of AI-native RAN systems. Indicative skills/experience: - Deep understanding of 5G/6G RAN architecture (O-RAN, NG-RAN
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Computer Vision There is growing trend towards explainable AI (XAI) today. Opaque-box models with deep learning (DL) offer high accuracy but are not explainable due to which there can be problems in
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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design
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to protect AI models against data leakage during inter-departmental information sharing. With the National Police heavily relying on sensitive data exchanges, this research will develop secure machine learning
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AI and machine learning applications in health research. Demonstrated ability to manage large datasets and develop predictive models. Excellent written and verbal communication skills. Strong
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Are you passionate about hardware security, side-channel analysis and machine learning? Do you want to contribute to research within an international environment? If the answer is yes, please