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Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience-informed design
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computationally efficient numerical structural models. To support the condition (state) assessment, the project will also explore the use of advanced estimators (e.g., Kalman Filter) or Machine Learning models
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on these materials, which will be used to implement high frequency and high efficiency IVRs for HPC applications. Objectives:-Fabrication, characterization and modeling of the composite magnetic materials.-Modeling
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• Strong quantitative and programming skills; experience with seismic data analysis or numerical modelling is highly desirable• Excellent written and verbal communication skills• Ability to work
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machine learning for next-generation wireless networks, (ii) Foundations of semantic communications and age of information, (iii) Stochastic geometry and spatial modeling of large-scale wireless systems
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with GKN Aerospace Sweden with partial work there and combines numerical and experimental research. Project overview The purpose of this project is to contribute to the development of an ultra-efficient
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Modern numerical simulation of spray break-up for gas turbine atomisation applications relies heavily upon the use of primary atomisation models, which predict drop size and position based upon
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the areas of fluid dynamics, turbulence and net-zero combustion. There is substantial scope for the student to direct the project with the main focus on (i) Generating an advanced Direct Numerical Simulation
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response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience-informed design platforms. Furthermore, REUNATECH
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, control), physical and numerical modelling (2D/3D) Independent research in the field of the dissertation topic Independent teaching, language of instruction German Collaboration in various research projects