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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 23 hours ago
fully autonomous materials discovery platform. Our lab operates at the intersection of materials science, robotics, and physical AI to accelerate green energy research. The autonomous platform screens
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to staff position within a Research Infrastructure? No Offer Description Description The Robot Learning & Control Lab (REAL Lab) at NYU Abu Dhabi is seeking an outstanding Post-Doctoral Associate
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build the sustainable companies and societies of the future. The Robotics and Artificial Intelligence , RAI, subject at the department of Computer Science and Electrical and Space Engineering at Luleå
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of experimental design. Identify high-impact research lines. Provide mentoring to researchers in training. Advice on personalized robotic technology. Where to apply Website https://seuelectronica.upc.edu/en
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that is changing the world. Connections working at Stony Brook University More Jobs from This Employer https://main.hercjobs.org/jobs/22076658/robotic-cardiac-surgeon-associate-x2f-assistant-x2f-professor
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construction of complex metallic components. Activities include planar and non-planar slicing, toolpath optimization, robotic integration, and experimental validation on aluminum alloys and structural steels
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and artificial intelligence to interpret vision data from manufacturing processes and systems. Design robotic systems that capture vision information from manufacturing machines and manufactured
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PolyU’s website at https://www.polyu.edu.hk/ . DEPARTMENT OF INDUSTRIAL AND SYSTEMS ENGINEERING Research Assistant Professor in Smart Manufacturing Systems / Physical AI / Embodied Intelligence (three posts
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develop interdisciplinary programmes in key strategic areas including precision oncology, advanced materials, regional oceanography, artificial intelligence and robotics, data science, cognitive and brain
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, robotics, and industrial systems. Building on recent work in reachset-conformant identification, the project focuses on constructing abstractions whose reachable sets provably overapproximate all observable