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) to develop accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models
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algorithms and system frameworks that optimize cost, performance, and scalability. The role focuses on leveraging machine learning and reinforcement learning to enhance storage and service efficiency under
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responsible for the end-to-end investigation of novel federated learning strategies for causal inference. The role will bridge rigorous theoretical work with hands-on algorithm design and development on real
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enhancements, including advanced navigation algorithms, swarm intelligence, cyber security hardening, and payload-specific control systems. Key Responsibilities: Control Augmentation Development: Design
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algorithms to extract biologically relevant crop traits from image and climate data, supporting mechanistic models for optimised light use and energy-efficient growth. Working closely with a modelling
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algorithm design. To seek and demonstrate practical utility of using AI for automating quantum algorithm design. To analyze complex wave-based physical systems and their high-dimensional parameter spaces in
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effectiveness of battery recycling workflows, supporting experimental and simulation studies across the battery lifecycle, and implementing algorithms for process monitoring and optimization so that recycling
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algorithms with surgical robotics. The RA will design and implement cutting-edge algorithms, and also be actively involved in the development AI tools tailored for different medical domains. These are efforts
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machine learning by designing and developing innovative models and algorithms. Key Responsibilities: Designing and conducting comprehensive research in artificial intelligence and machine learning, with a
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the research on Integrated Sensing and Communications (ISAC) with Reconfigurable Intelligent Metasurfaces (RIS). Key Responsibilities: Design the algorithm for Integrated Sensing and Communications