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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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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
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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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unique applied learning pedagogy integrates work and study, embedding authentic learning experiences within real-world environments. Through strategic partnerships forged by our faculty with industry