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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in challenging deep learning at its core? And
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Are you interested in challenging deep learning at its core? And specifically, do you want to perform cutting-edge research and develop novel advances in hyperbolic deep learning for computer vision
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representations. In this project, you will substantially improve quantitative magnetic resonance imaging (MRI) image quality using deep learning approaches. Quantitative MRI allows healthcare providers
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(MRI) image quality using deep learning approaches. Quantitative MRI allows healthcare providers to quantitatively assess and characterize the state of a tumour and its microenvironment. This information
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physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated against experimental observations Bridging scales from pore-level
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for a candidate with: an MSc in computer science, artificial intelligence or a related field a creative and collaborative mindset strong programming skills in Python or Rust strong skills in deep learning
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creative and collaborative mindset strong programming skills in Python or Rust strong skills in deep learning systems strong analytical and problem-solving skills fluency in English, both written and spoken
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experience with deep learning, machine learning and/or time series analysis. Good programming skills in Python or similar languages. Experience with using machine learning in the context of neuroscience
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investigate deep learning methods for local data augmentation and adaptive point density control, addressing the anisotropy and uneven sampling typical of urban LiDAR. You will work on a four-year doctoral
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creation that controls clogging patterns Developing predictive digital rock physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated