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department in the ISSA expertise center that develops advanced AI solutions involving AI models, algorithms, implementations, sensors and hardware for small scale edge up to large scale distributed and hybrid
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role involves designing and integrating real-time software algorithms with robotic hardware, including perception, control, communication, and safety modules to enable safe, precise, and reliable remote
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probabilistic generative models for networks; analyze real network data from different application domains; design efficient algorithmic implementations of the theoretical models. You will be supervised by Dr
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to develop a data-efficient and uncertainty-aware framework for pigment mapping in Cultural Heritage. Heterogeneous spectral and imaging data from different sensors will be jointly exploited, including X-ray
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(GPR), complemented by soil sensors and borehole data. A particular emphasis will be placed on the combined use of borehole and surface GPR, as well as small-scale EMI measurements in controlled
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both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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description The functioning of cities depends more than ever on urban infrastructures like transportation networks, power grids, water networks, Internet of Things sensors, and analytics platforms that gather
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-loss events undermine statistical confidence. The aim is to develop i) edge intelligence (on-turbine smart algorithms for data preprocessing), ii) resilient data movement (error-tolerant, cybersecure
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on the development of new sensor concepts, their experimental characterisation, the development of suitable methodologies and systematic validation under realistic operating conditions. This includes the setup