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Field
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simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power consumption and
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: Develop an event-driven RL algorithm that sparsely updates network state and parameters that will significantly improve energy to-solution efficiency compared to conventional digital accelerators when
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Virtual Training Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience
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. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material
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computer science and control systems architecture, advancing all the above disciplines. Building energy flexibility is an important resource for balancing and load shifting in energy networks, especially
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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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the development of a Virtual Training Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and
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This self-funded PhD research project aims to develop smart sensors based on low-frequency resonance accelerometers for condition monitoring of ultra-speed bearings. The developed smart sensors will
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materials. This class of materials has unique properties which make them promising candidates for next-generation electronic devices, energy storage systems, sensors, and catalysts. However, they also pose
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Europe | about 2 months ago
approaches for telecommunicationsHost institution: UNILIM, FranceSupervisors: Prof. P. Roy (UNILIM)DC 10 – OpenProject Title: Design of wavelength space division multiplexing MCF-based passive optical networks