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Field
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human activities, and a probabilistic analysis of the risks associated with their development by 2050. 1. Literature review of multi-scale modelling methods for cumulative effects Initially, a
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temporal scales from seconds to hours and beyond. The aim of this PhD project is to build a multi-scale model linking molecular renewal to functional properties of synapses to study the relationship between
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media. This axis is dedicated to the characterization of mono- and multi-polymer NPP suspensions — comprising polyethylene (PE), polystyrene (PS), and polypropylene (PP) — under conditions representative
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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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-scale numerical simulation framework to assess mechanical cloaking meta-structures within the ANR–FNR Metacloak project (Robust multi-scale design of meta-structures for mechanical cloaking from additive
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of data scientists, software engineers, and experimental researchers on topics including: Developing multi-scale and multi-modal representation learning methods for scientific imaging data (e.g., SEM
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of multi-scale composites using graphene/nanoparticles reinforcement in glass/carbon epoxy composites to increase the delamination resistance. Graphene/nanoparticles, due to its nano dimension, can reinforce
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temporal scales from seconds to hours and beyond. The aim of this PhD project is to build a multi-scale model linking molecular renewal to functional properties of synapses to study the relationship between
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About the project: Multi-scale modelling of Thin Film Deposition with Flowable CVD for Next-gen Semiconductors Supervisor: Dr Bora Karasulu, University of Warwick This project, co-funded by Merck
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This PhD position is part of the ErUM-Data project CmarT, which aims to develop a novel multi-scale imaging approach at synchrotron radiation facilities, particularly PETRA III at DESY. Biological