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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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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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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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, 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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; 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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(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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), 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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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
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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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flow behaviour, droplet formation, freezing dynamics, and ice crystallisation effects, integrating experimental data to refine predictive models for process performance and construct stability. Task 3