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area, have extensive experience in conducting model-based economic evaluations using suitable statistical software (e.g. R or Python) and the ability to work independently, prioritise own workload and
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or machine learning. Excellent programming skills (e.g., MATLAB, Python, or ROS), a strong publication record, and an ability to work collaboratively in multidisciplinary environments are essential. Prior
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assistantship. The ideal candidate will have a strong understanding of NLP, machine learning, and AI ethics, with proven skills in Python and frameworks like TensorFlow or PyTorch. Preference may be given
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tools (STATA, R, or Python). Assist in literature reviews and summarising academic research. Contribute to writing research papers and policy reports. Participate in meetings with collaborators and
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languages such as Python for data analysis and simulations. Experience with optical systems, astronomical observations, or satellite tracking would be advantageous, although not compulsory. Strong analytical
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experience in developing computational models and implementing models for computer simulations. Software development in C++ and/or Python is expected, and experience in model analysis and parameter
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with coding, ideally in Python or MATLAB Funding support This studentship is open to Home students only. It is jointly supported by the Faculty of Engineering and industrial partners which is expected
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or Abaqus (or willingness to learn) General knowledge of structural analysis and material behaviour, especially failure mechanisms Some experience with coding, ideally in Python or MATLAB Funding support This
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-field methods) Multiscale mechanics and microstructure-property relationships Python/C++/Matlab-based simulation and data analysis Industry-facing research and technology transfer You will also benefit
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will be an advantage if candidates have an interest in optimisation, control and computing systems, model checking, mathematical logic and good programming skills, ideally in MATLAB, Python and/or C/C