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Field
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you wonder how AI and sensor technology are changing our view of animal welfare and the human-animal relationship? And do you want to explore the opportunities and dilemmas that this technological
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for the work in the section. This in particular involves adopting the Industry4.0 framework and establishing the digital twin of manufactured products by the combination of process monitoring by sensors and
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to develop a data-efficient and uncertainty-aware framework for pigment mapping in Cultural Heritage. Heterogeneous spectral and imaging data from different sensors will be jointly exploited, including X-ray
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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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that does not reflect local reality and thereby create unfair comparisons. Your work will include measurements of greenhouse gases (CO2, CH4, N2O) using drones and various sensors, as well as remote sensing
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mobility and vulnerability to falls. The project will combine physiological sensory biomarkers with postural control, gait analysis and real-life mobility measurements obtained using wearable sensors and
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of electron spin pairs in chiral molecules created by light using diamond-based quantum sensors. Spin dynamics of these radical pairs are central both in biology and for quantum information science applications
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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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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