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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 co-simulation
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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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project, “Mathcrete”, and offers a unique opportunity to collaborate closely with another Postdoc specializing in 3D nano image reconstruction and modelling tools development, as well as other PhDs and
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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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-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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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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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