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Field
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of the event-based RL learning rule. Benchmarking criteria include accuracy, latency, data efficiency, and energy consumption to reach a learned solution on small robotic control tasks. Your tasks in detail
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in crystalline rocks. Drilling optimization using machine learning, e.g. predicting rate of penetration (ROP) and wear. Investigate the possibilities in automation and robotization and the use
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machine learning, e.g. predicting rate of penetration (ROP) and wear. Investigate the possibilities in automation and robotization and the use of artificial intelligence. Electric drilling and other methods
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leverage advanced bespoke continuum robotic systems to demonstrate the feasibility of applying the proposed coatings can be deployed in-situ. Ultimately, this work bridges the gap between the theory
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strong background in industrial engineering, computer science, software engineering, energy systems, robotics, or related disciplines Interest in AI, simulation, and optimization for energy and industrial
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to mitigate damage to these TBCs. Moreover, this project will leverage advanced bespoke continuum robotic systems to demonstrate the feasibility of applying the proposed coatings can be deployed in-situ
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Application deadline: All year round Research theme: Robotics, Smart Manufacturing How to apply:uom.link/pgr-apply-2425 No. of positions: 1 This 3.5 year PhD is funded by the Department
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, mathematics, or statistics. Practical or theoretical knowledge in automated synthesis/robotics. N.B. The candidates selected for interview will receive a preliminary assignment containing a short programming
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together world-class expertise in textiles, materials, soft robotics, biomechanics, sports, healthcare, machine learning and AI, with globally leading industrial and academic partners. Your Project
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. Real-World Validation: Deploy and benchmark your algorithms on our autonomous vehicle, mobile robots, and UAV testbeds. You will: Publish in CVPR/ICCV/ECCV, NeurIPS/ICLR, INFOCOM/ISIT and leading IEEE