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are developing AI that can map welding processes in the automotive industry based on simulation data. We are looking for a student assistant with an interest in machine learning and finite element simulation. Your
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loading conditions. Finite Element models of the designed specimens will also be generated and biomechanical behaviour of the tooth restored complex will be predicted. Supervisors Prof Hassan Ghadbeigi
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skills. You’ll have strong skills in Finite Element Analysis (e.g. Abaqus, ANSYS), an understanding of machining processes, and a proactive, collaborative approach to problem-solving. The University
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structure for integration into cardiac catheters. Conduct finite element analysis (FEA) to simulate force/strain responses under clinical conditions. Assist in the setup and execution of in-vitro and in-vivo
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Python, MATLAB, C++, Julia, or similar Furthermore, experience within collaborative code development and usage of version control tools like git mixed finite element methods iterative numerical methods
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application of machine learning to biomedical data ï‚· Background in computational modeling for neurobiology, including finite element modeling (FEM) or system identification methods in medical applications
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printing, rapid prototyping, Finite element analysis (FEA), Multiphysics analysis, Design for composite materials, material science Preferred skills/experience areas include: Finite element analysis
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systems. Experience with NQA-1 quality assurance requirements for nuclear facilities. Knowledge of AWS welding codes and standards. Familiarity with finite element analysis (FEA) software, such as ANSYS
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the supervisor Qualifications A recent PhD in Mechanical Engineering or a related field, focusing on Thermofluids Experience in Numerical Simulation (Finite Element Analysis and/or Computational Fluid Dynamics
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(e.g., finite element or wave propagation simulations) for defect detection and materials analysis Integrate AI, machine learning, and robotics into NDE and manufacturing processes for automation and