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, incorporating systems engineering principles, real operational data, and control system modelling • Validate the industry partner’s digital twin model and identify performance gaps through real-world
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for part-time employment. Starting date: 14.01.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date
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junctions, and mechanosensitive signaling pathways impaired by P1f deficiency. In the in vivo part, the therapeutic potential of mini-plectins will be tested in a newly established P1f knockout mouse model
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-year PhD project aiming to unravel the mechanisms that control the development of functional and persistent young mangrove ecosystems for nature-based shoreline protection. The research will investigate
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Four-year (48 months) Research Scientist (senior Postdoc level) position: Where: The Charles Institute of Dermatology (https://www.ucd.ie/charles/ ), School of Medicine (https://www.ucd.ie/medicine
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monitoring and control. The research will design and evaluate hybrid AI + physics-based optimization frameworks that combine the accuracy and interpretability of physics-based optimization with the speed and
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-theory-based and recently proposed Moiré Plane Wave Expansion approaches. A significant part of the project is focusing on the development of novel machine learning protocols and workflows based on a large
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to study grid stability, fault propagation, and recovery dynamics. Analyzing control and protection strategies using high-resolution time-domain models. Developing dynamic models for grid-forming and grid
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on electrophysiological approaches (MEEG, iEEG) and signal processing, while in Maastricht, the partner team provides ultra-high-field imaging (7T and 9.4T fMRI) and AI-based modeling. The PhD student will be enrolled
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(Lua/Java), agent behavior modeling, event handling, and API-based integration with external AI systems. Experience with distributed systems, reinforcement learning, or simulation environments (e.g