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that amplify human potential. The successful candidate will engage in innovative research projects in ML, focusing on developing novel ML algorithms, enhancing human-AI collaboration, and exploring systems
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/ML algorithms, specialized neural processing hardware and design tools, and ML security, and their applications in resource-constrained Embodied-AI systems (like autonomous vehicles, UAVs/UGVs
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, and cognitive systems and intelligent vision systems, including efficient learning and inference of complex AI/ML algorithms, specialized neural processing hardware and design tools, and ML security
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for building energy-efficient and robust brain-inspired, autonomous, and cognitive systems and intelligent vision systems, including efficient learning and inference of complex AI/ML algorithms, specialized
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processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large
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for building energy-efficient and robust brain-inspired, autonomous, and cognitive systems and intelligent vision systems, including efficient learning and inference of complex AI/ML algorithms, specialized
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role in supporting research design, algorithmic development, data analysis, and coding tasks across CTED’s interdisciplinary projects. Working under the direction of the Principal Investigator (PI), Dr
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and intelligent vision systems, including efficient learning and inference of complex AI/ML algorithms, specialized neural processing hardware and design tools, and ML security, and their applications
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design, development, and validation of material, control systems, and algorithms for next-generation soft haptic actuators and experiences. Note that the research involves significant interactions with