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Bachelor's degree in Physics or other related field. Fluency in finite element simulation, familiarity with machining. Preferred Qualifications Experience in experimental low temperature physics, familiarity
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-time data acquisition during production, consulting, and prototype manufacturing. Graph neural networks provide an opportunity to operate on Mesh structured data utilized in Finite Element Method (FEM
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computational facilities at the laboratory. Development of finite element technologies that enable accurate and computationally-efficient simulations. Generate sources of funding for fundamental and applied
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moving through different fluids. In this project, we are interested in developing moving mesh finite element methods for their dynamical simulation. We aim to produce efficient, accurate and robust
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for downhole milling tools. Conduct Finite Element Analysis (FEA) of cutting mechanics under downhole conditions. Support design and validation of prototype tools using the company’s bespoke instrumented test
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and carry out finite element method (FEM) simulations. Our developments focus on higher efficiencies, more cost-effective manufacturing processes and materials, improved long-term stability and new
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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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for success: A PhD in Mechanical Engineering, Materials Science, Physics, or a related field. Experience in computational mechanics, finite element analysis, and numerical simulations. A strong background in
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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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to plasticity. (assessed at: application/interview) Experience in computational mechanics, especially numerical methods for solving field equations relevant to material mechanics, i.e., Finite Element schemes