Experience and know-how: strong theoretical and practical background in machine learning and deep learning; solid understanding of industrial automation concepts, control systems, as well as real-time or embedded environments; proficient programming skills in Python and C++; familiarity with functional safety standards (IEC 61508, ISO 13849) as well as formal verification or validation methods; proven experience in deploying and optimizing AI models on edge devices, embedded hardware, or resource-constrained platforms; knowledge of model optimization techniques such as quantization, pruning, knowledge distillation, or accelerated inference; practical experience with PLC environments, industrial communication protocols (OPC UA, PROFINET, EtherCAT), or industrial real-time platforms is desirable
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