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of the ECHO-EMG research initiative, funded by the Independent Research Fund Denmark (DFF). The project aims to develop a novel system that combines high-density surface electromyography (HD-sEMG) and
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degradation modes. Evaluating suitable sensor technologies and data sources for acquiring relevant metrics. Developing tools and algorithms to automatically analyse sensor data, assess asset condition, and
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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This project aims to develop outcome measures for early detection of neurodegenerative disorders using artificial intelligence (AI) and various sensing modalities, offering personalised support to
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, such as imbalance and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict
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slicing. - Develop advanced AI/ML algorithms and data analytics techniques to automate and optimise exposure requests, adapted to available resources and real-time demand. - Propose and
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ambitious new project on the safe deployment of medical imaging AI. The purpose of the role is to conduct world-leading research on AI for medical imaging. The goal is to develop methodologies and
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vulnerabilities like side-channel attacks and unauthorized access, which can compromise system integrity. Developing robust security measures within AI-enabled electronics is essential for applications in defence
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sustainability. The research will delve into power-aware computing strategies, thermal management, and the development of algorithms that balance performance with energy consumption. Students will aim to create
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-critical systems. The research will focus on developing AI-powered verification tools, health monitoring algorithms, and compliance assurance techniques that ensure system reliability throughout