You design and operate scalable, MLOps-driven optimisation pipelines, enabling training, deployment, and inference across diverse embedded targets and accelerator architectures. * University degree in electrical engineering, computer science, data science, artificial intelligence, mathematics, physics, or a comparable qualification. * Strong expertise in embedded AI, particularly in neural network architectures, quantisation, hardware‑ and quantisation‑aware training, as well as deployment formats and cross‑platform portability. * In‑depth expert knowledge of hardware/software co‑design for ML systems, including cross‑layer optimisation, architecture search, benchmarking, and the design of neural accelerators. * Comprehensive expertise in modern machine learning methods, including deep learning, multimodal models, end-to-end learning and transformer architectures, combined with a ...
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