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utilizing real-time data for dynamic risk evaluation; (3) the advancement of risk-to-resilience methodologies; and (4) the establishment of digital twin-based resilience frameworks for CI, HTI, and urban
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with researchers at DTU and KTH, you will help develop an integrated decision-support system that: Uses real-time sensor data and AI models to assess risk scenarios. Dynamically recommends optimal
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Position Description The Unsteady Flow Diagnostics Laboratory (UNFoLD) led by Prof. Karen Mulleners at EPFL in Lausanne is looking for multiple PhD students to join the group in the fall of 2025 or early
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on the expression but also the dynamic interaction of the key proteins for the endocannabinoid signalling and to target these systems in a cell-type specific manner to test whether their modulation might counteract
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opportunity to contribute to one of our ongoing projects addressing questions such as: What roles do ion channels, cytoskeletal dynamics, and axonal transport play during axonal injury and neurological diseases
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aim to visualize the endocannabinoid system using advanced chemical tools focusing not only on the expression but also the dynamic interaction of the key proteins for the endocannabinoid signalling and
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, and (iv) Next-generation positioning, navigation, and timing. The selected candidate will also support research proposal development, dissemination, and student mentoring efforts within a dynamic
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dynamics, data science, and machine learning are beneficial. Please submit your detailed application with the usual documents by August 15, 2025 (stamped arrival date of the university central mail
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holistic multi-hazard risk framework capturing cascading effects across systems and scales; (2) the creation of digital environments utilizing real-time data for dynamic risk evaluation; (3) the advancement
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SQL databases and file repositories. We are now taking the next strategic step: developing ontologies and a dynamic knowledge graph to semantically link our internal data systems - and connect them