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- UNIVERSITY OF VIENNA
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governed by UK higher education regulations, and the European campus of Northeastern University – a large, top-tier research intensive, Boston-based institution. Founded in 1898, Northeastern received
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training and collaboration across historical, social science, legal, and data science methods, including Earth Observation analysis and the large-scale analysis of survivor narratives. Applicants proposing
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The continuous release of Earth Observation (satellite) data and the emergence of Machine Learning methods open up new possibilities for understanding forests. These large datasets provide
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Despite significant advances in numerical techniques and computing hardware, the high computational cost of large-scale 3D computational fluid dynamics (CFD) modelling remains a major challenge. A
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and large models, limiting real-world deployment. This PhD focuses on efficient Physical AI, emphasising data-efficient training, reinforcement learning, continual adaptation and edge deployment
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This PhD asks a different question: instead of demanding more data, can we build language models that learn smarter from less? You will design AI architectures that adapt to the structure of a
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currently unavailable to clinicians. To unravel the hidden information, the student will apply advanced computational approaches to large-scale clinical datasets collected during VT ablation procedures
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profiles, language proficiency and accent familiarity Exploring how AI, speech models and large language models can support prediction of comprehension and listening effort Developing validated models and
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. Experience of working with large multimodal datasets. Interest in human-computer interaction and human-centred system design. Strong communication and organisational skills. While it is not necessary to have
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that extract material properties or defect information from measurements. You will work across the full research pipeline: running large-scale ultrasound simulations to generate rich training datasets, designing