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porous media. Proficiency in scientific programming (Python, C++, or Fortran) is essential. Demonstrated strong analytical and problem-solving skills, with familiarity in numerical methods and an interest
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, stiffness loss, damage evolution, and transient creep interact under coupled loading. The project will develop temperature-dependent constitutive models informed by numerical simulation. Machine learning
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with thermal/heat‑pipe simulation. The work will establish a verified neutronics baseline, develop a thermal/heat‑pipe model, and integrate the tools to quantify temperature feedback and enable rapid
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: machine/deep learning, numerical modelling, statistics, optimisation, scientific computing • Ability to work across disciplines and collaborate with academic and industrial teams Desirable: • Experience in
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numerical simulation is desirable. Experience with computational tools such as ANSYS Fluent, STAR-CCM+, or COMSOL Multiphysics would be advantageous. Good oral and written communication skills with