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image analysis. The position offers opportunities to develop independent research projects, contribute to collaborative grant applications, and prepare for an independent academic or industry career in
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much of their signal. Radar and thermal cameras are less affected but give sparser or noisier data. Reliable localisation therefore needs several sensor types and a good understanding of how each of
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prescriptive maintenance system for Genie’s lifting equipment. The project focuses on connected, partially connected, and non-connected machines, transforming telematics, onboard sensor data, and historical
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of research involves developing AI models that can learn to represent real-world phenomena based on various types of observations, including video, audio, and sensor data. These AI models move beyond
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AI-based Digital Pathology (AiDP) Programme. The programme focuses on the development and validation of advanced AI methodologies, including image processing, computer vision, and mathematical
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observations from our spatially distributed sensor network (ITEM) to airborne and satellite image observations through near-surface and atmospheric radiative transport process modelling. You will
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modifying sensitivity, as well as the utility of modulation techniques (temperature, light, voltage) for enhancing selectivity. We are also interested in effects relating to the contacting of sensor
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. Key Responsibilities: Develop, implement, and optimize machine learning/deep learning models for digital pathology image analysis Analyze large-scale histopathology, omics, and clinical datasets
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nonlinear imaging techniques to study, characterize and image water in different configuration. Introduction Water is the liquid of life. It is intimately linked to our well-being. Without water, cell
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ever higher spatial resolution, even minute positioning errors, mechanical drift, and sample deformations become sources of image degradation. The central challenge is to reconstruct accurate 3D and 4D