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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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project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
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validating data, spinning up virtual machines for research groups and enabling access to the relevant data as appropriate, providing support for researchers using the environment and dealing with any data
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data gaps by combining process simulation (e.g., Aspen software) with machine learning techniques. By developing accurate, large-scale life cycle inventory data using enhanced digital tools like deep
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: 30 June 2025 Details Join a project that will combine physics, machine learning, and ultrasonics to design new sensors for the digital revolution in industry. Ultra-thin membranes are produced in many
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analytics techniques (machine learning) for process control and optimisation. In this project, you will focus on achieving metamaterial behaviour through phase control within the additive manufacturing build
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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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Forecasting the Future of Biodiversity: Cutting-Edge Approaches to Population and Community Dynamics
: How can tools like passive bioacoustics revolutionize wildlife monitoring? We offer cutting-edge training in statistical modelling, machine learning, and ecological forecasting, and our lab works across
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of robotic equipment within bespoke cells to improve the efficiency and performance of machines. Provide guidance, assistance and technical advice with programming and integration of associated software and
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(XAI) to enhance the reliability and applicability of AI algorithms for healthcare applications [2] and/or identifying pitfalls of current AI models using adversarial machine learning. Supervisor Bio Dr