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reactive power coordination, and stabilize grid operations by enhancing collaboration between grid controllers at different system layers. Fair price incentives and market participation for reactive power
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of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the MR signal into the training of the INR network, we aim to compensate for the effects
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the consortium; the candidate's MSCA-DN mobility eligibility for the selected host country. The selected candidate will therefore be matched with the TUAI partner offering the best scientific and supervisory fit
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to investigate the potential of using Implicit Neural Representation (INR), a class of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the
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interpretable framework for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured
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suspension pipe flow by 30–40% across different flow rates. This makes designing and controlling these flows difficult. The difficulty arises partly because these flows do not fit the conventional ‘laminar
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metrology tools is continuously challenged by stricter requirements and increasingly complex geometries. In this project, you will work on the ideation, modeling, and experimental validation of novel
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for a single ecosystem. The geological actualism approach in this project is to check: which exactly modern subcommunities of forest plants and herbivores have distinctly different baselines
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modelling for conversion and sorbent regeneration, in conjunction with another PhD student in the department who will perform CFD modelling and other researchers performing process system modelling within
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leader is Associate Professor Soledad Gonzalo Cogno. About the project The successful candidate will contribute to the development of mathematical and computational models to enquire about the mechanisms