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Field
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mHealth systems integrating smartphones and wireless sensors with signal processing algorithms and artificial intelligence (AI) models for unobtrusive sleep apnea detection, multi-night monitoring
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use, and long-term crop management goals. While several algorithms have shown promising results in energy savings and crop yield, most of these methods have only been tested in simulation, and make use
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directions include: Using AI to learn parameters and design circuits for quantum optimization algorithms (e.g., beyond QUBO formulations for QAOA) Developing AI-driven methods to discover quantum optimization
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changing operating conditions. This PhD project focuses on developing AI-supported decision methods for routing and scheduling in transport and logistics systems. The research investigates how operations
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different incentives, mechanisms and regulatory scenarios, evaluating their performance with respect to different objectives and their trade-offs. Developing decentralized and bi-level algorithms to help
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research on post-quantum cryptographic algorithms and protocols, assisting in the design, implementation, testing, and evaluation of secure cryptographic solutions resilient to quantum attacks, and
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to operate in a scalable and decentralized manner while achieving obstacle avoidance using onboard sensing and adapting to changes in dynamic environments. In parallel to the theoretical and algorithmic
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operation will be studied. From a methodological perspective, the above research challenges will be tackled through a mix of theory, algorithm design, and analysis of experimental data, partly collected by
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user studies, develop novel algorithms, build immersive/augmented realities, and validate your solutions in real-world settings. This PhD is ideal for candidates interested in one or more of the
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experimental validation of control strategies for the developed systems. This includes the development of advanced control algorithms capable of handling varying operating conditions, ensuring system stability