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equations. Strong knowledge in optimization and numerical analysis. Some experience on numerical analysis of stochastic (partial) differential equations. Additional Information Work Location(s) Number
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live in. Your role To this end, one PhD student will be hired to perform research in the domain of quantum computing applied to optimization problems with possible topics covering: Variational quantum
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” focusing on the effect of a fluctuating environment on the collective dynamics of self-propelled agents, a numerical part on “reinforcement learning” focusing on optimizing communication between agents in a
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microstructure of bone tissue. This PhD project will investigate lattice structures designed to achieve optimal dynamic performance, as for instance advanced dampers to mitigate unwanted vibrations, or high
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to original material properties depending upong thermal and photo stimulations through the development of numerical models for simulation. Numerical simulation enables precise analysis and prediction
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than conventional structures. Superstructure optimization, which uses a predefined architecture with numerous unit operations and possible paths, offers an alternative for identifying non-intuitive
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the final phase, the study focuses on the advanced optimization of the composite by integrating functional nanostructures, particularly carbon nanotubes (CNTs). Due to their nanoscale cylindrical structure
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reconfigurable. This task requires extensive coupled optimization (piezoelectro-mechanical), hence computational, sensor/actuator and metamaterial expertise is of key importance. This PhD programme will be
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mechanical motion into electricity; - An intelligent electronic control system, enabling optimized regulation of the thermodynamic cycle. Within the CALIFORCE3 framework, the REMLA system will be integrated
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experimental protocols and numerical models, encompassing steady-state analyses and advanced techniques such as Laplace and Fourier transforms. Furthermore, inverse modeling methods were implemented to estimate