187 algorithm-development-"Multiple"-"Prof"-"Prof"-"Simons-Foundation"-"U.S" Fellowship positions at Nanyang Technological University
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The Continental-NTU Corp Lab invites applications for the position of Research Fellow. Key Responsibilities: Lead the development of situation awareness, interaction behaviour modelling and decision
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advances the mathematical foundations, algorithms, and real-world applications of epistemic uncertainty in machine learning, with a strong focus on imprecise probabilities, uncertainty representation and
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on complex system subject to various constraints. Design and develop real-time algorithms. Simulate and evaluate system performance. Document research outcome to publish at international conference / journal
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. The successful candidate will play a pivotal role in a project centered around variational quantum algorithm in the near-term, especially on innovating advanced error mitigation or detection techniques to solve
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novel research methodologies in computer vision, deep learning architectures, and neuro-fuzzy systems to contribute to the development of robust AI frameworks for medical diagnosis and treatment support
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economics Develop state-of-the-art algorithms in algorithmic game theory and fair division Design and conduct experiments to evaluate the algorithm performance Job Requirements: PhD degree in computer science
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The Continental-NTU Corp Lab is a strategic research collaboration between NTU and Continental, focusing on developing technically advanced solutions in the areas such as sustainable materials
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The Continental-NTU Corp Lab is a strategic research collaboration between NTU and Continental, focusing on developing technically advanced solutions in the areas such as sustainable materials
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state-of-the-art facilities to work on the following: Developing advanced path planning, search, and exploration algorithms for multi-UAVs systems in unknown and complex 3D environments. Designing
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems