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Field
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combine ultrafast pump–push–probe experiments with sub-10 fs resolution, finite-element simulations, and quantum models extending the Tavis–Cummings Hamiltonian. The aim is to demonstrate coherent control
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in-house design. Strong ability to use simulation software tools including finite difference time domain methods (FDTD), eigenmode expansion methods (EME), or finite-element methods (FEM), using tools
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propagation in tools used in Friction Stir Welding (FSW) of advanced steel pipelines. The research activities include: Development of thermo-mechanical finite element models for Friction Stir Welding tools
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verified through simulated and/or laboratory-generated data. Responsibilities and qualifications Your primary responsibilities and tasks include: Development and advancement of multi-fidelity finite element
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structures is essential. Experience with finite element, finite difference or discrete element modelling (e.g., FLAC3D, 3DEC, Abaqus, COMSOL or similar software) is considered an advantage. The successful
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) - Mining Engineering - Solid Mechanics - or a closely related discipline A strong background in numerical modelling of rock and underground structures is essential. Experience with finite element, finite
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, the development and validation of finite element models, and the formulation of analytical and design-oriented models. Building on this work, you will contribute to complementary investigations on the combined
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classification. The work will involve analytical studies, computer simulations, and finite element (FE) analysis, alongside experimental development and validation. Entry requirements The minimum entry requirement
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PhD in Advanced Testing, Modelling, and Simulation of Composite Materials under High Velocity Impact
positions for general criteria for the position. Preferred selection criteria Experience in solid mechanics and non-linear finite element analysis. Experience in optical measurements related to experimental
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CAD tools Finite-element analysis and simulation Rapid prototyping and additive manufacturing Signal processing and data analytics, control systems Machine learning or computer vision Product