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enabling precise and rapid identification of adulteration. Spectral techniques generate unique chemical fingerprints of food items, which machine learning algorithms analyse to detect inconsistencies and
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will contribute to areas such as the design and analysis of algorithms (e.g. randomized, quantum, approximation, property testing, online, streaming, sublinear, fine-grained, distributed/parallel) and/or
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the simulation and optimization of distributed systems, for instance by specializing neural ODEs and their training routines. This research will address challenges driven by the energy transition, which is
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-channel measurement systems, transmitted via high-speed links to a server for algorithm-based processing. The candidate will contribute to the design of efficient server-side data processing and participate
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Communications Surveys & Tutorials. 2021 Oct 4;23(4):2525-56. · Kshemkalyani, Ajay D., and Mukesh Singhal. Distributed computing: principles, algorithms, and systems. Cambridge University Press, 2011. · Convery
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. IEEE Communications Surveys & Tutorials. 2021 Oct 4;23(4):2525-56. · Kshemkalyani, Ajay D., and Mukesh Singhal. Distributed computing: principles, algorithms, and systems. Cambridge University Press
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apply machine learning algorithms with special attention to digital footprint reduction and data privacy. Functions to be developed: Develop methodologies and experiments to measure and optimize
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into embedded prototypes to demonstrate real-world feasibility. The overarching goal is to bridge high-level algorithmic innovation with energy-aware hardware deployment, enabling intelligent sensor systems
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probability distribution known only up to a normalization constant, is fundamental in Computational science, engineering, and Bayesian statistics. Recent research suggests that gradient flow-based algorithms in
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, and energy efficiency—and meeting these demands will require smart, distributed computing built directly into the network. This research project focuses on designing AI-native edge computing systems