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called Tiramisu [1]. Unlike existing frameworks, Tiramisu can perform advanced code optimizations that are hard to apply otherwise. Because of this, Tiramisu can generate fast code that outperforms highly
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code optimizations that are hard to apply otherwise. Because of this, Tiramisu can generate fast code that outperforms highly optimized code written by expert programmers and can target different
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to theoretical approaches in machine learning for real-world applications, with a preferred focus on optimization, data-efficient machine learning, approximate inference, statistical machine learning, continual
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practical applications of advanced machine learning techniques. Emphasis will be given to theoretical approaches in machine learning for real-world applications, with a preferred focus on optimization, data
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methodologies tailored to the lab’s hardware/software testbed. Integrate SDRs (e.g., USRPs, RFSoC) and RF front-ends (FR2, FR3, V-band) into end-to-end ISAC demonstrators. Implement and optimize advanced signal
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-Aware Optimizations for QML, QML Security, Error Correction for Quantum Computing, Secure Quantum-Classical Systems, Privacy-Preserving Quantum Computing, and Fingerprinting for Quantum Computing. Strong
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Circuits, Robust and Efficient Mapping of Quantum Algorithms on Quantum Machines, Quantum Noise-Aware Optimizations for QML, QML Security, Error Correction for Quantum Computing, Secure Quantum-Classical
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RF front-ends (FR2, FR3, V-band) into end-to-end ISAC demonstrators. Implement and optimize advanced signal processing algorithms for joint communication and sensing. Analyze experimental datasets
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-Aware Optimizations for QML, QML Security, Error Correction for Quantum Computing, Secure Quantum-Classical Systems, Privacy-Preserving Quantum Computing, and Fingerprinting for Quantum Computing. Strong