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experience in manufacturing systems modeling, simulation (i.e., DES), and digital twins. • Good knowledge and experience in machine learning, reinforcement learning, and AI-based optimization for production
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: · Learning how to express software requirements precisely using formal models. · Using these specifications to automatically generate test cases for software systems and code. · Exploring how test
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a Research Infrastructure? No Offer Description This position is part of HEPARD MSCA European funded doctoral network (https://cordis.europa.eu/project/id/101226455 ). The project is training early
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analyses), unit operations (comminution and separation processes) as well as system engineering approaches (modeling and simulation, geometallurgy). Experimental work is carried out in our well-equipped
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(comminution and separation processes) as well as system engineering approaches (modeling and simulation, geometallurgy). Experimental work is carried out in our well-equipped mineral processing laboratory with
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implementation of Hierarchical Modelling of Species Communities (e.g. HMSC) to analyse biodiversity-environment-disturbance relationships and simulate community responses in abyssal seascapes Producing open-access
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schemes and identify the application in larger HVDC systems. Expected Results Definition of relevant scenarios for the use cases, inspired in realistic European systems. Detailed simulation models
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pathology, microbiology, modeling, bioinformatics, biostatistics and biophysics for the quality and health of horticultural species and seed production. As part of the IRHS, the ImHorPhen (Imaging
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, implementation, validation and assessment of new solutions, optimization techniques and AI/ML models to support edge intelligence and the next generation of mobile communication networks (5G+/6G) with focus
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computational science expertise. The Computational Science (CPS) Division focuses on solving the most challenging scientific problems through advanced modeling and simulation on the most capable computers