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Field
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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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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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) learning numerical methods for wave-equation-based processing, imaging, and inversion. Wave phenomena are ubiquitous in science, and they extend to objectives ranging from global Earth discovery, to natural
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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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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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development can alter tissue mechanical properties and allow cancer cells to withstand compressive forces within a tumour environment. This PhD project aims to map keratin network architecture in epithelial
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for automotive and aerospace applications. Mechanical characterization tools such as quasi-static load frames with various capacities, high-rate servo-hydraulic load frames, compression and tension split Hopkinson
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Aerial Vehicles (UAVs) sent to perform a mission, e.g. search and rescue operations, intelligent transportation systems and wireless sensor networks. The data can vary from image and video, GPS, GSM
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evaluation, and technical advisory. Position Summary As a Machine Learning Research Scientist in the Frontier Lab, you will conduct applied AI/ML research and develop prototype capabilities that inform and