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Field
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robotic systems and AI models. You will learn how to programme advanced robotic systems and how to implement aspects of deep learning and neural networks for chemical property prediction. You will be part
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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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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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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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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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) , applications open from October 1st 2025 to December, 1st 2025 (11:59 p.m., EST) Robotics , applications open from October 1st 2025 to December, 10th 2025 (03:00 p.m., EST) Software Engineering (SE
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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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. 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
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under varying lighting, fabric blends, and soiling; (5) porting the inference pipeline to an embedded/edge-compute platform; (6) integrating with our robotic pick-and-place cell for iterative field trials
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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