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transparent and intelligible. Although explainable AI methods can shed some light on the inner workings of black-box machine learning models such as deep neural networks, they have severe drawbacks and
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workings of black-box machine learning models such as deep neural networks, they have severe drawbacks and limitations. The field of interpretable machine learning aims to fill this gap by developing
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challenges (such as raw material constraints, hydrogen availability, and infrastructure deployment challenges), and analyze deep uncertainties. The research will guide sustainable transition strategies
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, identify challenges (such as raw material constraints, hydrogen availability, and infrastructure deployment challenges), and analyze deep uncertainties. The research will guide sustainable transition
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particular in deep learning, LLM, digital hardware design, embedded systems, audio processing; Proficiency in deep learning frameworks (e.g. PyTorch) and programming skills (SystemVerilog, Verilog, Python, C