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multimodal data. Your responsibilities include: Developing and applying machine learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing
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functionalisation strategies, and cutting-edge characterisation techniques (including Manchester Met’s unique in-situ electrochemical-AFM-nanoRaman (TERS) system) to optimise sensor performance. The research will
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protoDUNEs, were built and allowed to test three different reading technologies. Two technologies have been selected for the first two remote sensor modules: the first, wire-based, and the second, PCB-based
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. Your main responsibilities will include: Fly drones and process data of multispectral, thermal and LiDAR sensors Implement approaches to integrate drone data into an existing agroecosystem data
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welfare are monitored on farms. However, many current monitoring systems rely primarily on visual or activity-based sensors, meaning important behavioural and physiological signals may be missed. Sound
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manufacturing machines and advanced mechatronic systems, rely on ultra-reliable, low-latency, and deterministic communication between distributed sensors, controllers, and actuators. Ensuring such performance
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project, we are looking for a talented PhD candidate that can establish novel processes for fabrication of 3D pyrolytic carbon nano- and microelectrodes and integrate them into sensor platforms
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and sensor fabrication, host of precision electrical/optical/biochemical characterisation techniques, simulations and first principles theoretical calculations of these advanced materials. In addition
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description The functioning of cities depends more than ever on urban infrastructures like transportation networks, power grids, water networks, Internet of Things sensors, and analytics platforms that gather
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. • Experience in assembling, adapting and maintaining experimental devices, sensors, chambers, field structures or other environmental monitoring equipment. • Good organizational skills, autonomy and problem