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systems capable of understanding, learning, and acting in complex, dynamic settings. The team works at the intersection of computer vision, multimodal learning, and robotics to create next-generation
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seeks to appoint an Associate Research Scientist. Motivated applicants with a strong background in Machine Learning, Robotics, Haptics, and interest in leading cross-disciplinary research to study
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Junior Research Scientist in the Center for Quantum and Topological Systems (CQTS) – Dr. Hisham Sati
arises. Applicants must have a Bachelors in one of the following: Computer Science, Computer/Electrical/Communication Engineering, Mathematics, Physics. For consideration, applicants need to submit a cover
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, quantitatively intensive research at CTED with a primary focus on artificial intelligence, machine learning, mathematical modeling, and computational analysis. The Junior Research Scientist will play a central
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, Optimization, Machine Learning, Reinforcement Learning, Computer Vision, and Pattern Recognition, as well as contributing to curriculum development. About NYU Abu Dhabi https://nyuad.nyu.edu/en/ NYU Abu Dhabi is
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with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running natural language processing and machine learning workflows; (3) experimental design
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Junior Research Scientist in the Center for Quantum and Topological Systems (CQTS) – Dr. Hisham Sati
/Electrical/Communication Engineering, Mathematics, Physics. For consideration, applicants need to submit a cover letter, curriculum vitae with full publication list, transcript of degree, statement of research
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practical applications of advanced machine learning techniques. Emphasis will be given to theoretical approaches in machine learning for real-world applications, with a preferred focus on optimization, data
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to contribute to cutting-edge research in robot intelligence, machine learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms
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working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI systems