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be used to demonstrate the ability to detect both gradual degradation and faults in the machine. Training & Skills You will benefit from a taught programme, giving you a broad understanding of the
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; computer vision; and machine learning. Ability to initiate, plan, organise, implement and deliver programmes of work to deadlines. Ability to work with people from different backgrounds and in team across
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Adopter programmes, where appropriate. We are looking for an enthusiastic, motivated graduate in (health) Data Science, Computer Science or related field who is interested in the deployment, user acceptance
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to develop and validate paediatric bone models through a combined computational and experimental approach, in collaboration with clinical partners at the Sheffield Children’s Hospital. The ideal candidate will
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distinctive environment for the support and training of Medical Research Council (MRC) funded PhD students across our partner institutions. You will work closely with the Doctoral Training Programme manager to
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. (assessed at: application & interview) Desirable criteria Knowledge of computer programming with experience in a scientific program language e.g., Python, Java, C++, C, C#, LabVIEW, MATLAB, Halcon, R, Maple
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research staff members working in Algorithms, Computational Complexity, Combinatorics, Logic, Program Semantics and Verification, etc, and is one of the strongest and most diverse groups of its kind in all
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(PPPA) group at the University of Sheffield, in collaboration with other institutions and industrial partners, pursues a wide programme related to these muon applications. This PhD project is
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reference the application criteria in the application statement when you apply. Essential criteria Postgraduate qualification in a relevant subject such as a PhD or MSc in Psychology, Data Science, Computer
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diseases, combining biomechanical experiments, micro-Computed Tomography imaging, Digital Volume Correlation, and Nanoindentation. The position is funded as part of the project “ChildBone: A novel digital