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The project involves building and curating a comprehensive food image dataset suitable for mobile AI applications. High-accuracy deep learning models will be trained on this dataset and then
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. This research focuses on developing and implementing compressive sensing methods for electron microscopy and spectrometry based imaging and microanalysis techniques to address some of the inherent data
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environmental impact of cloud infrastructures. Context and Motivation The deployment of AI applications is undergoing a paradigm shift with the advent of 5G/6G networks, the Internet of Things (IoT), and edge
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Management, and IT). The research conducted at the L2S focuses on the fundamental and applied mathematical aspects of control theory, signal and image processing, information theory, and communication
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to enquiries, and delivering high‑quality customer service to staff and students across a range of channels. You will play an important role in presenting a positive image of the University, tailoring messages
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Your position • Maintain and enhance pipelines for spike sorting, calcium imaging signal extraction, neuron tracking across recordings, and automated behavioral analysis. • Develop efficient data
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geometry preprocessing/compressing in the presence of geometric singularities and coupling the obtained discretizations to the wideband fast multipole method based accelerators and direct solvers
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within the laboratory environment, including relevant experience in the safe handling/storage/disposal of biological waste and hazardous substances (e.g., chemical waste, compressed gases, and cryogens
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joint commercialisation of new wearable sensor technology for clinical compression therapy applications. Your primary task will be the design, build and delivery of a prototype portable and field
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controlled compression techniques. The project will explore both internal and external jugular veins as accessible and reliable markers of central venous pressure. Research objectives: The PhD candidate will