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-UAS technologies o Multi-agent systems and swarm robotics o Autonomous navigation and guidance o Computer vision and perception o Machine learning
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hardware design using Verilog/SystemVerilog or equivalent HDLs. Sound knowledge of machine learning algorithms and their hardware implementation considerations. Demonstrated ability to conduct independent
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experience in, for example, machine learning, high-performance computing, reconfigurable hardware, cloud computing, compiler engineering, distributed systems, or DevOps are encouraged to apply, as
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, fabrication materials and orbital environments. Using a small number of representative test cases, we will trial Machine Learning-based predictive methods to estimate material condition and indicative reuse
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in robotics, machine learning, battery systems or a related discipline to deliver project outcomes, build productive research partnerships and contribute to high-quality outputs. You will have the
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your expertise in robotics, machine learning, battery systems or a related discipline to deliver project outcomes, build productive research partnerships and contribute to high-quality outputs. You will
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experience in, for example, machine learning, high-performance computing, reconfigurable hardware, cloud computing, compiler engineering, distributed systems, or DevOps are encouraged to apply, as
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. ISO 13485, ISO 14971, IEC 60601, UK MDR) is desirable, as well as experience of applying signal processing or machine learning methods to physiological or sensor data. Application details: Applications
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. Knowledge of data-driven analytics, machine learning, signal processing, or advanced modelling techniques relevant to power systems. Experience with real-time simulation platforms, hardware-in-the-loop
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-science and signal-analysis tasks, including processing experimental signals, integrating datasets, developing machine-learning models, and mapping measured fuel properties to SAF performance. For the post