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on electrical and calorimetric efficiency measurement has the primary emphasis of: measurement-system design, sensor characterization, uncertainty analysis, converter testing and calorimeter development. The best
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scientific initiative focused on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and
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in order to recruit 11 outstanding PhD students. The MoM section is recognized worldwide for its high-level research on experimental analysis, theoretical understanding and predictive modelling
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information by encoding many noisy physical qubits into fewer, far more reliable logical ones. Without effective QEC, adding qubits does not add computing power, because errors accumulate faster than machines
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control applications? In this PhD project, you will develop methods for the analysis, verification, and design of neural-network-based controllers. Rather than treating safety and stability as properties
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evidence generation and reassessment. You will focus on how international HTA bodies anticipate it will shape their activities in the coming years. Assessing the extent to which GenAI tools can reliably
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PhD students. The MoM section is recognized worldwide for its high-level research on experimental analysis, theoretical understanding and predictive modelling of complex thermo-mechanical behaviour (e.g
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into fewer, far more reliable logical ones. Without effective QEC, adding qubits does not add computing power, because errors accumulate faster than machines can scale. Despite impressive hardware progress
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in domains where observations are indirect, incomplete, expensive, or noisy, and where reliable models must respect the structure of the underlying physical system. For example, in subsurface
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also be considered. Your research will determine which optical measurement techniques can reliably capture deformation and damage under cryogenic conditions, how experimental constraints can be overcome