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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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, advanced materials, and sensor development, while also addressing key robotic challenges such as real-time process monitoring and diagnostics in inaccessible or hazardous environments. We have discussed a
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Fixed term – until 31/3/2026 Full time - 37 hours per week Closing date 27/05/2025 at 23:30 Sheffield Hallam University is recruiting for a Researcher in Advanced Sensor Technology to support the
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the University of Southampton, where fibre optic sensors are being developed to measure strain in real time during the cure process. As part of this exciting project, we are collaborating with our spin-out company
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-time systems. The role will involve working with large and multi-modal datasets (e.g., images, video, audio, and sensor data), and deploying solutions in real-world environments, particularly in robotics
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mapping using a team of highly mobile legged or legged-wheeled robotic platforms. The research will investigate advanced algorithms for multi-robot coordination, dynamic path optimization, and collaborative
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building a UAV capable of quantifying aerosol size, composition and radiative effects in the Upper Troposphere-Lower Stratosphere (UTLS). Lead the selection, integration, and testing of advanced sensors
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environment-specific algorithms and machine learning approaches. At the end of the project a technology demonstrator will be built using UAV- and USV- mounted radar sensors, and it will be tested in real world
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, adapt existing and develop new methodologies in robotics, design working algorithms from theories, deploy and test algorithms on hardware as appropriate, and analyse data from a variety of sources and
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administrative activities, adapt existing and develop new methodologies in robotics, design working algorithms from theories, deploy and test algorithms on hardware as appropriate, and analyse data from a variety