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and Key Distribution, Spectrum Management and Coexistence, Tactile Internet, Earth Observation, and Autonomous Transportation. As far as technical enablers are concerned, we leverage expertise
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tree. In fact, the problem being NP-hard, a handful of aircraft is enough to make it unsolvable in polynomial time. The work requires theoretical studies on the state of the art, together with algorithm
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methodologies in brain diseases. The candidate will work on developing advanced new algorithms, testing and validation, and applications in these data modalities. The candidate will have the opportunity to work
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algorithms aimed at predicting pathogen potential based on whole or partial genomes. You will also be responsible for integrating such tools into online accessible pipelines thus, providing the possibility
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Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis algorithms for critical equipment (e.g
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scalability and resource efficiency through the development of cooperative, distributed AI algorithms, optimising data, energy, and processing resources while adapting to the different computational
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in collaboration with other researchers in the Distributed Intelligent Systems and Communications (DISCO) group at the Department of Mathematics and Computer Science. DISCO is a leading
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verticals, Unmanned Aerial Vehicles, Integrated Satellite-Space-Terrestrial Networks, Quantum Communications and Key Distribution, Spectrum Management and Coexistence, Tactile Internet, Earth Observation, and
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, and evaluation in distributed and privacy-aware settings. While the position is supported by an AI for Science project on privacy-preserving federated learning, the broader objective is to advance
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application! Work assignments This position focuses on the development of theoretically grounded and practically scalable decentralized learning algorithms under realistic system constraints, including