190 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof" "UNIS" Fellowship positions at Nanyang Technological University
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, quantum machine learning, quantum algorithms from well-established universities/institutes. The candidates must be highly motivated in multidisciplinary research. He/she must have proven experience in
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Centre for Advanced Robotics Technology Innovation (CARTIN) is looking for a candidate to join them as a Research Fellow. Key Responsibilities: Develop novel algorithms for multi-agent inverse
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researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
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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
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on AI-driven end-to-end autonomous driving algorithms. Key Responsibilities: The research fellow will be leading the development of AI-driven end-to-end autonomous driving algorithms. The work will
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problem-solving skills, with a focus on developing innovative solutions for multi-modal LLMs. Self-motivated and able to work independently, managing multiple tasks and projects in a fast-paced environment
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cooperative, competitive, and mixed settings. Collaborative decision-making frameworks and decentralized learning algorithms. Adaptive, meta-learning, and context-aware strategies to enhance policy
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of performance, speed, and precision. Key Responsibilities: Design and implement genAI models for embodied AI systems. Develop and optimize deep learning algorithms to enable robotic arms to perform complex tasks
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operation. Develop modular architectures for multi-agent coordination, sensing, and communication. Integrate sensor suites, flight controllers, and swarm coordination algorithms into UAV platforms. Conduct
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(terrestrial and NTN). The goal of this research is to design and develop algorithms and techniques that adapt to the environment, minimizing signaling overhead associated with channel estimation and enhancing