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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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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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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
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An outstanding, motivated, enthusiastic, curiosity-driven researcher. Deep analytical skills, initiative, creativity, and flexibility are highly desired. An MSc degree in Mechanical Engineering, Materials Science
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predictive digital rock physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated against experimental observations Bridging scales
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Kusterer), marketing strategy (Gerrit van Bruggen), deep learning (Sebastian Gabel), consumer and firm networks (Xi Chen), customer analytics (Aurélie Lemmens), and consumer learning (Maciej Szymanowski
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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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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Do you have a background in deep learning and computer vision? Are you
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Do you have a background in deep learning and computer vision? Are you independent, creative and eager to take initiatives? Do you enjoy working in an international research group and interacting