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the University of Cambridge, UK. The Research Assistant will work together with a team of students and research collaborators on the development of learning-based control policies that facilitate the coordination
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this project, we aim to: Develop real-time ultrasound algorithms to estimate fascicle length in antagonistic leg muscles (tibialis anterior and soleus) in healthy individuals during walking. Translate and
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learning theory to join the research team of Prof. Muhammad Umar B. Niazi. The position focuses on the design and implementation of incentive mechanisms for sociotechnical and cyber-physical-human systems
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, spinal cord injury etc) through real-time neural control of wearable robotic exoskeletons. You will be developing next-generation (low and high-level) control algorithms for wearable exoskeletons that use
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an extensive array of resources: UAVs for communication protocol, edge computing and control algorithm development; THz transceivers for high-frequency communication channel measurement and sensing; Real-Time
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implementation of various projects, including (but not limited to) scaling, algorithm development, scaled score development and documentation. Support the creation, management, and retention of large-scale data
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an extensive array of resources: UAVs for communication protocol, edge computing and control algorithm development; THz transceivers for high-frequency communication channel measurement and sensing; Real-Time
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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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Scientific Machine Learning. The successful candidate will develop and deploy state-of-the-art SciML algorithms in high-performance computational physics codes. We accept applications from all candidates with
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! Neuromorphic computing is a way to do computations with hardware that mimics the architecture of the brain. This project aims to explore and benchmark the potential of new neuromorphic hardware and algorithms