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, resilience, and sustainability of stormwater management. The research will focus on integrating AI and machine learning with hydraulic-hydrologic modeling, urban planning strategies, and nature-based
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-making. However, traditional machine learning models face limitations in this domain due to several critical challenges. First, ICU data are high-dimensional and multimodal, with patient states evolving
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well as candidates with a background in machine learning methods. The PhD programme will straddle the boundaries between the field of wave modelling and the general field of machine learning, and we will set up a team
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underground conditions. Apply machine learning and AI techniques to enhance model accuracy and optimize design parameters. Contribute to the development of a comprehensive, AI-based design methodology for LUS
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biodiversity or occurrence data (e.g., GBIF). Understanding of species distribution modelling or trait-based ecology. Interest or experience in applying AI or machine learning methods to ecological questions
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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criteria Demonstrated knowledge and experience in electrical system modeling and analysis, applied control, power electronic systems, optimization techniques, and/or machine learning. Experience with
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Experience in computational cardiovascular biomechanics Experience in medical image segmentation Experience in Python programming or a similar programming language Experience with machine learning models Good
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relevant experience in the development and deployment of machine/deep learning models as well as the use of remote sensing data You must have relevant experience in the development of hydrodynamic and water
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, training deep learning models to adapt designs to boundary conditions, and integrating FEM workflows within parametric modeling environments like Grasshopper. The candidate will contribute to building a