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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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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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, and entrepreneurship. Doctoral Candidates will gain transferable skills and learn from industry role models, equipping them to make significant contributions to solving the AMR crisis. The succsesssful
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on ROS2 (Robot Operating System) and best practice of use of Github. Knowledge and skills on methods in numerical optimization, machine learning, as well as knowledge on marine power and control systems