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experience of treatment. The overarching aim of the project is to use machine learning methods to understand why many people who are referred for treatment will drop out prematurely. To do this, two studies
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of acoustic wave propagation in moving fluid and physics-based machine learning (ML) methods. Support experimental design in the laboratory, carry out data processing and to use the experimental results
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science, computer vision, medical/image analysis is essential. Experience of research (or interest in) in one or more of the following: deep learning; big data management; computational pathology; medical imaging
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supports the semi-automated checking of research outputs, including machine learning and Artificial Intelligence models. Working closely with the Research and Innovation Platforms Team, you will oversee
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global castings industry. The AMRC Castings Group is a leader in advancing casting technologies and techniques. Our team provides advanced casting expertise, including computer process modelling, design
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. To fill in this gap, in collaboration with industrial partners, the research will develop novel Machine Learning and Computer Vision methods for detecting and localising. These will be used to develop
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/interview) A degree (Bachelor’s, Master’s, or PhD or equivalent experience) in Software Engineering, Computer Networks, Embedded Systems, AI, Data Engineering, or a related subject (assessed at: application
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to evaluate computational properties and train heterogeneous networks of devices applied on challenging real-world tasks. This post will develop state-of-the-art machine learning models to develop and
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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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collaborative programme of research funded by the Aerospace Technology Institute (ATI) with several Industry partners, including Airbus, GKN and Renishaw. Critical for the implementation of additive manufacturing