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method (FEM) simulations using metamodeling techniques and Machine Learning (ML). By enriching datasets and leveraging advanced simulations to optimize ML models, we seek to enhance manufacturing
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or semester interns. Your profile PhD in Materials Science, Mechanical Engineering, Manufacturing Technology or similar fields Experienced in CFD and FEM simulations using tools such as OpenFOAM (preferably
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Particle Hydrodynamics (SPH) or Finite Element Method (FEM), which require significant computational resources and expert knowledge. These traditional methods lead to prolonged development cycles and
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simulations based on Smoothed Particle Hydrodynamics (SPH) or Finite Element Method (FEM), which require significant computational resources and expert knowledge. These traditional methods lead to prolonged
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simulations based on Smoothed Particle Hydrodynamics (SPH) or Finite Element Method (FEM), which require significant computational resources and expert knowledge. These traditional methods lead to prolonged
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and FEM simulations using tools such as OpenFOAM (preferably) /COMSOL/ Abaqus etc., with a strong focus on multi-phase modeling of high-energy density heat sources, including laser-material and electron
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-time process control. This project aims to overcome this limitation by integrating real-world casting data, process parameters, and finite element method (FEM) simulations using metamodeling techniques