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at least one programming language (ideally python). Experience in medical data processing is advantageous. Knowledge of CI/CD practices (e.g., git), containers (docker, singularity, or similar) and
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realistic degradation from corrosion processes. The simulations will be integrated with mesoscale experimental to evaluate the constitutive response of smooth specimens degraded by corrosion. Given
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, ultimately optimising the deposition process. Additive manufacturing (AM) is a rapidly advancing technology, driving numerous innovations and finding diverse applications across industries such as aerospace
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and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that continues to drive
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This PhD opportunity at Cranfield University invites candidates to explore the integration of AI into certification and lifecycle monitoring processes for safety-critical systems. The project delves
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costs. Condition monitoring (CM) of rolling element bearings, hereafter called bearings, has been the main point of attention for many decades in the industry for maintenance. This is because bearings
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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relevant discipline/subject area. A minimum of English language proficiency (IELTS overall minimum score of 6.5). Also, the candidate is expected to: Have excellent analytical, reporting and communication
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the development of specialized hardware architectures capable of efficient, real-time processing. Embedded AI hardware architectures, including neuromorphic processors and low-power AI accelerators
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professional network spanning academia, industry, and national research centres. Through this multidisciplinary project, the student will develop expertise in: Contribute to the development and operation of