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Learning, Algorithms, Noise Handling (Error Correction/Mitigation), and Verification. These roles are part of the Quantum Software Lab (QSL, link: https://www.quantumsoftwarelab.com ), in collaboration with
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and evaluation. The post holder will take a leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis
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, including: Robot Learning: Creating algorithms that empower robots to learn autonomously from interactions and adjust to new tasks. Manipulation: Enhancing techniques for precise and adaptable object handling
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project aims to address the current limitations of traditional frame-based sensors and associated processing pipelines with a new family of algorithmic architectures that mimic more closely the behaviours
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and communications (ISAC), and their applications (particularly in securing and rural scenarios) , and will be expected to support Dr. Zhuangkun Wei and Prof. Hongjian Sun. The post holder will
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and communications (ISAC), and their applications (particularly in securing and rural scenarios), and will be expected to support Dr. Zhuangkun Wei and Prof. Hongjian Sun. The post holder will
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. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development. Responsibilities will include programming, analysing and
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the coordination of large-scale robot systems (ground and aerial). The ideal candidate will possess hands-on experience with designing and implementing reinforcement learning algorithms, and deploying them onto real
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain
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, estimation, and identification algorithms that directly interface with physical hardware. We work closely with industry partners. Our research has led to several methods now used in commercial products. We