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involving different decarbonisation technologies, as well as the development of AI-driven decision-making tools for optimising low-carbon marine powertrain designs. This position is part of the Horizon Europe
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methods for eliciting and aggregating safety specifications and risk thresh-olds for AI systems, with a particular focus on mechanisms that provide axiomatic guarantees, are algorithmically tractable, and
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project aims to address the current limitations of traditional frame-based sensors and associated processing pipelines with a new family of algorithmic architectures that mimic more closely the behaviours
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-of-concept for a groundbreaking real-time dose verification system which is adaptable to different proton therapy treatment systems. This proof-of-concept detector system will be tested under
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large arrays are individually controlled, interrogated, and even entangled with other atoms. We seek to leverage the many-fold technical QIS advances to develop new algorithms for optical clocks as
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Responsibilities Develop suitable algorithmic methods for live and real-time analysis of synchronous and asynchronous data. Write research reports and publications. Analyse and interpret the results of own research
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positions are attached to four different collaborative projects with related but distinct goals, all making use of our expertise in squeezing-based entangled states of light: ClusterQ (ERC): A platform for
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and
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aims to optimize the operations (serving) of AI by developing algorithms that manage compute, network, and storage resources in a carbon-efficient way while supporting long-term benefits
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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 19 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different