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Field
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PhD/Postdoc position in trustworthy data-driven control and networked AI for rehabilitation robotics
human-robot interaction) and communications (in networked control systems). Many of the developed methods are experimentally validated in our multi-robot lab. Your qualifications: We are searching
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characterization of plant fibers by using a robot with a small tip that can compress the plant fibers one by one. These works demonstrated the capability to efficiently generate experimental tests but, we now want
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Vision–Language–Action (VLA) models for tabletop and mobile manipulation. A key focus is leveraging whole-body, humanoid-style motion to improve perception and control, enabling robots to solve complex
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
field, with a strong background in experimental or theoretical/computational aspects of neuroprostheses, wearable robotics and human sensing technologies; demonstrated skills in embedded systems, control
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for greenhouses and vertical farming systems. The successful candidate will focus on robot hardware development, automation system design, machine vision, and intelligent control, supported by physics-based
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and environmental conditions. Candidates should have a strong publication record in mobile robotics. A strong focus on sensing and control systems, demonstrated through hardware-validated experiments
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-learning based exoskeleton controllers to work across tasks Designing and validating new robotic lower-limb prostheses Exploring other high-risk high-reward research areas related to device design, control
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Are you interested in Robotics and can you contribute to the development of the project Dynamics and Control of Robotic Handling and Maintenance of Fusion Reactors? Then the Department of Mechanical
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platform integrating advanced robotics, materials characterization systems, digital twins and AI-based decision-making algorithms for accelerated research. The platform will incorporate explainable AI
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to joint research activities, publications, and surveys. Requirements PhD degree (or near completion) in robotics, control, machine learning, or a related field; Strong publication record demonstrating