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Field
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interdisciplinary character. The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
for automated, data-driven diagnostics, integrating AI with high-resolution imaging and sensing offers a transformative solution. AI models can learn to recognize subtle damage patterns, enabling faster, more
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1-dimensional ocean-ice model. You will identify any long-term changes in ocean CO2 uptake along the Antarctic Peninsula using data from Rothera, SOCAT (www.socat.info) and mapped CO2 products, while
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implementing models that integrate ecological dynamics, species traits, phylogenetic trees, and economic discounting; ● Devising Bayesian or POMDP frameworks to handle uncertainty about species interactions
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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dynamic environments, utilising Vision, Language, and Action (VLA) models. The candidate will focus on designing novel training regimes and/or novel architectures for learning in embodied environments
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Modeling: Develop detailed mathematical models to describe and predict polarization behavior in array antennas. These models will serve as a foundational tool for understanding key factors affecting
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-home rates. You will learn a wide range of molecular modelling techniques, as well as collaborative software development and computational skills providing expertise for a broad range of future careers
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supervision at Technische Universiteit Eindhoven) over a period of 36 months. We are seeking a highly motivated PhD candidate to join our research team working on mathematical modelling of electrochemical
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Why apply? Do you want to shape the future of trustworthy AI and work at the intersection of foundation models, multimodal data, and explainability? This PhD project offers you the chance to develop