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operability, including prediction of critical phenomena such as water hammer. The methodology will be verified against industrial data regarding performance and operation. You’ll join a multidisciplinary team
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, computer vision or flow measurement background. Prior experience in computational modelling is beneficial, but not mandatory. Similarly, experience of computer coding in some form or any discipline is also
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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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, multidisciplinary PhD research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle
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their problem-solving, data analysis, and critical thinking abilities, as they work on real-world aerodynamic challenges. In addition, the student will refine their communication skills by presenting research
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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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using learning algorithms as Extreme Learning Machine (ELM) is that training data should cover the entire domain of process parameters to achieve accurate generalization of the trained model to new
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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens