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Field
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tasks The scope of the PhD position(s) fall within the areas of novel low-complexity neural network architectures, generative audio techniques, and the integration of large language and speech foundation
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive
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of error-controlled biomechanical models in SOFA / FEniCSx / SOniCS for real-time use on AR devices Design of Bayesian neural-network surrogates and graph-based models for tissue deformation and brain shift
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to about the size of a drinks can. In this project we will use emerging 3D visual sensing technologies such as implicit neural rendering (NeRF, Gaussian Splatting) and Geometric Foundation Models
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of Materials, analytical and numerical Data-Driven Engineering Design and Optimization Algorithms Surrogate Modeling (e.g., Kriging, Gaussian Processes, Neural Networks, etc.) Scientific Programming (e.g
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Learning, particularly Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models, including hands-on implementation Strong understanding of machine learning models
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for this shift. Recent advances in small language models, TinyML frameworks, sparse neural networks, and microcontroller-grade accelerators now make it feasible to deploy sophisticated reasoning and self
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science, environmental modelling, geosciences, or related field with strong quantitative focus; Strong background in machine learning methods such as neural networks and transformers; Knowledge on handling
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spaceborne remote sensing. You will first identify large-scale drivers of compound extremes in models and observations, then build an emulator using advanced AI methods, such as convolutional neural networks