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Physics based machine learning algorithm to assess the onset of amplitude modulation in wind turbine noise (with TNEI Group)
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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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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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-resolution imaging and reconstruction of neural tissues (see https://ist.ac.at/en/research/siegert-group/). Leveraging computational tools such as machine learning and topological data analysis, we will
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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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Robust machine learning using information theoretic approaches for damage detection in complex machines (C3.5-ELE-Esnaola)
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Effective and Efficient Visual Presentation of Machine Learning Outputs Derived from High-Dimensional Data to Clinicians (S3.5-SMP-Alix)
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subsystems • High performance tunable and reconfigurable oscillators and frequency synthesisers • Application of AI / Machine Learning to physical layer circuitry, signals and waveforms Researchers can expect
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various applications. Potential applications include environmental monitoring, process manufacturing, machining, scientific characterisation, and renewable energy systems. The outcomes of this research will
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Topologically constrained physics-informed machine learning for modelling complex spin textures (S3.5-COM-Ellis)