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Field
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driving systems; assists in the integration of sensors and perception algorithms; assists in the design of software pipeline and experiments; performs and monitors experiments and data collection; assists
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Details A brain-computer interface (BCI) provides a direct communication pathway between a human brain and an external device. Using appropriate sensors and data processing algorithms, a BCI maps patterns
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of control systems along with their related software, hardware, and equipment. These systems control and monitor air handlers, pumps, valves, temperature sensors, pressure sensors, boilers, chillers, and other
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on the use of Machine Learning algorithms for rapid damage assessment. Research topics could focus on: the definition and use of novel damage sensitive features, physics informed machine learning, transfer
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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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and Durham University. The primary focus will be on designing and implementing deep learning and anomaly detection algorithms to analyse large-scale, real-world sensor data collected from in-service
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/ machine learning algorithms to support research in the IDMxS Analytics Cluster. The RF will apply/ improve machine learning algorithms to process (e.g., classify, predict) data collected by IDMxS. Help
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nanocomposite and supramolecular materials for next-generation integrated sensor arrays. This role supports innovation in intelligent sensing technologies, contributing to the development of scalable
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, as well as sensors, actuators, and control architectures that are particular to the mechanical and process engineering disciplines. Successful candidates should hold a PhD degree or equivalent in
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computing, domain-specific multi- and manycore architectures, networks-on-chip (NoCs), methods and algorithms for application parallelization, simulators and virtual platforms for application- and