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complex nonlinear models, with unique research opportunities in algorithm and hardware co-design, particularly for resource-constrained environments. Applications focus on edge intelligence
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Scaling analog in-memory computing towards large generative AI models H/F Subject Your work will span device-to-algorithm co-design, focusing on state-of-the-art small language model engineering and
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languages ( e.g. , Python, C++) for algorithm development, simulation, and data analysis Background in real-time signal processing ( e.g. , time-series data, filtering, feature extraction
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Liège. Your tasks in detail: Develop an event-driven learning algorithm for latent reasoning that sparsely updates network state and parameters in complex reasoning scenarios. Explore hardware