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-quality research results in top-tier venues spanning machine learning, reinforcement learning, embodied AI, and AI in education (e.g., NeurIPS, ICML, ICLR, ACL, CoRL, IEEE ICRA/IROS, AIED, EAAAI
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evaluation of human-centered artifacts. This would include working on research projects involving assistive technologies for people with visual impairments, personalized learning platforms, and Human-Computer
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skills, and proficiency in machine learning frameworks such as Python, C/C++, TensorFlow, and PyTorch. 4. Hands-on experience with ultrasound systems, particularly Verasonics or similar platforms is
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robotic middleware (e.g., ROS, MoveIt) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic
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of resistive switching mechanisms in meta/insulator/metal memristive materials Qualifications • PhD in Computational and Applied Physics • Proficiency using software for the simulation of atomic structure
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(MIT, SMART) and Prof Saif A. Khan (NUS, SMART) and work with their team of researchers and collaborators. Requirements Candidates with an earned PhD degree in Chemical/Materials Engineering, ideally
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reporting to Prof. Patrick S. Doyle (MIT, SMART) and Prof Saif A. Khan (NUS, SMART) and work with their team of researchers and collaborators. Requirements -PhD degree in Chemical/Materials Engineering
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of teaching and learning. Successful candidate will bridge the fields of AI development and education research, working closely with other AI researchers in M3S to contribute in both the creation of generative
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on improving the potency, safety, and scalability of manufacturing cell therapies. In particular, this position is focused upon next-generation methods to manufacture CAR-T cells for cancer treatment. This PDA
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disorders. Assist in developing and validating machine learning-based predictive models using multimodal data (neuroimaging, clinical, biomarker, and demographic data). Support the design and implementation