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analysis, AI algorithm modeling, testing, and integration into functional systems within the project scope. Specifically, in activities related to behavior modeling from IoT device data, generative AI
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Simulation Group at ICN2 conducts cutting-edge research in computational materials science, focusing on electronic structure methods, atomistic simulations, and multiscale modelling. The group develops and
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and refine the RG-based model to enhance its biological interpretability and robustness across different tumor types; to extend the model to simulate and predict solid tumor response to innovative
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the design and optimization of multistatic and multifrequency radar architectures for near-field 3D imaging. - Contribute to the electromagnetic modeling of radiating systems, wave-object interaction, and
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building to make it more energy-efficient can lead to an increase in radon concentration. Modelling radon generation and transport in the source media, its entry into a building and its distribution
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professional opportunity, but also being close to a stimulating environment with all our research lines in simulation and numerical modeling, manufacturing and experimental testing of composite materials, design
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thermal management in energy conversion and data processing electronic devices. Fundamental understanding of nanoscale heat transport remains out of reach, and current models beyond diffusion are not
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) or closely related field. Process simulation : Aspen HYSYS/Plus. Optimization skills: experience with GAMS or pyomo (or similar), including model formulation. Life Cycle Assessment (LCA): experience with, e.g
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/PhD) or related field. - Simulation & data: TRNSYS (or similar), time-series processing; Python (pandas/numpy). - Experience with GIS and/or climate/solar datasets (e.g., METEONORM, PVGIS
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FOR DRAWING UP OF PREDOCTORAL CONTRACTS FOR THE TRAINING OF DOCTORAL STUDENTS FUNDED BY THE UPV'S RESEARCH STRUCTURES – SUBPROGRAMME 2 (PAID-01-22) 119865 Development of machine-learning and graph-based models