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staff position within a Research Infrastructure? No Offer Description We are seeking a highly motivated doctoral student to develop ship physics-integrated machine learning models for real-time prediction
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. The objective of the research is to use machine learning methods to find models of ship trajectories and traffic patterns that can be used to detect anomalies and predict into the future. The basis for this is
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and rinsing equipment (vats, fermentation tunnels); 3) Develop an intelligent AI-based control system capable of: Predicting hot water demand based on the winery's calendar and weather conditions
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, DeepFDM, MINO, etc., but also other methods for generative models in function spaces. Develop multiscale (resolution-invariant) AI models for wave kinematics and sea loads on ships, considering also phase
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resources in natural and managed ecosystems; enhance agricultural and forest productivity and sustainability; predict and mitigate impacts of environmental and climate change on ecosystems and society; and
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; machine learning methods (i.e. supervised and unsupervised learning, deep learning, reinforcement learning, etc.); artificial intelligence methods (e.g., predictive modeling, natural language processing
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), making them ideal membrane materials to realize selective and unidirectional ion transport. We will combine theory and prediction, chemical design, and on-water/liquid surface synthesis, as well as in-situ
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(BI) aims to develop computational models and calculation tools capable of predicting the structural behaviour of elements produced by accelerated carbonation. The work will integrate experimental data
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model by integrating the newly developed approaches into the numerical program developed in the ‘OceanCoupling ’ project. We would like the successful applicant to start in the first quarter of 2026
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safety. The goal of this PhD project is to increase knowledge of thermodynamic and material processes in marine hydrogen propulsion systems and to develop predictive models that ensure safe and efficient