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statistical and machine learning techniques for dynamic energy system modelling Develop advanced optimization algorithms for building energy management and control (e.g., MPC, RL) Develop and evaluate digital
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industrial and academic partners. The overall goal of the project is to optimize the design of water eletrolyzers for efficient green energy production. You will be conducting Computational Fluid Dynamic (CFD
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industrial and academic partners. The overall goal of the project is to optimize the design of water eletrolyzers for efficient green energy production. You will be conducting Computational Fluid Dynamic (CFD
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with plant-wide process analysis, renewable energy systems and green fuels, advanced monitoring, and fluid dynamics will be advantageous. Writing funding proposals and project management will also be a
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of cementitious materials. Experience with X-ray or neutron tomography. Flexibility is essential. We are looking for a team player who can also work independently, who is motivated to help and be part of a dynamic
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research related to fluid mechanics, structural dynamics, or related fields. Knowledge of experimental testing, calibration procedures, and data analysis for wave-related research. Strong scientific writing
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-based simulation for process optimization. Developing advanced numerical models for the coupled heat and mass transfer in the float zone process including 3D computational fluid dynamics (CFD) models
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systems (MEMS and NEMS). Prof. Vincenzo Esposito leads the project in the framework of a dynamic team in the Functional Oxides section. The FOX team possesses excellent competencies and the most advanced