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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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your PhD, you will gain deep expertise in Generative AI, specification engineering, and empirical software analysis. You will also publish in top venues, collaborate with leading researchers and develop
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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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on the analysis of pre-implantation kidney biopsies using deep learning and AI-driven image analysis. You will: - Analyse pre-implantation kidney biopsies according to the Banff criteria; - Apply AI methods
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/phd-in-micromechanical-experime… Requirements Specific Requirements An outstanding, motivated, enthusiastic, curiosity-driven researcher. Deep analytical skills, initiative, creativity, and flexibility
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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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using deep learning and AI-driven image analysis. You will: - Analyse pre-implantation kidney biopsies according to the Banff criteria; - Apply AI methods for automatic segmentation and morphometry
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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
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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