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Improving the clinical success of porous orthopaedic designs – experimental and finite element study
organisational skills, and the ability to work as a team player. Proficiency in Finite Element Analysis software (e.g., Abaqus, Ansys), image processing techniques, and programming languages such as Python and
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for microscopy or high-content imaging datasets.* Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow or scikit-learn.* Experience with model validation, reproducible workflows and
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, biomedical engineering, medical imaging, or related field. Experience in deep learning with practical implementation. Strong Python skills and relevant frameworks. Experience with large clinical imaging
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collaborative projects developing AI for sign languages. These include SignGPT, a five-year EPSRC Programme Grant with the University of Oxford and UCL building tools to translate automatically between
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, biomedical engineering, medical imaging, or related field Experience in deep learning with practical implementation Strong Python skills and relevant frameworks Experience with large clinical imaging datasets
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, prior distributions and posterior predictive checks, model comparison, programming in R (python/Matlab), implementations using R-packages rstan/JAGS and brms/STAN or equivalent interfaces. References