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Field
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will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show challenging properties of uncertainty, irregularity and
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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simple identification of nuclear material even when the typical signatures of the materials may be unavailable. X-ray imaging is commonly used to image concealed objects but x-rays are attenuated in
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. Although advances in imaging technologies have enhanced the spatio-temporal resolution of plankton sampling, these data remain virtually unused in biodiversity assessments and policy frameworks
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effective conservation policies. Although advances in imaging technologies have enhanced the spatio-temporal resolution of plankton sampling, these data remain virtually unused in biodiversity assessments and
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The research in this doctoral opportunity will investigate the relationship between material elastic and thermal properties by using high resolution digital imaging under dynamic loads. Digital
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Aerial Vehicles (UAV), e.g. drones, are increasingly used for equipment anomaly and fault detection in offshore wind turbines. When the drones are employed to take images, the quality of the images can be
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Student Development The successful applicant will gain expertise in: Biophysical imaging (AFM, microscopy) Microbiology and molecular biology RNA sequencing and data analysis Interdisciplinary collaboration
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will be imaged across different field and thermal histories using MFM, magneto-optical Kerr effect (MOKE) imaging, and synchrotron-based X-ray magnetic circular dichroism (XMCD). In parallel, electrical
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Methods Soft lithography and microfabrication to create defined surface landscapes. • Mating assays between resistant and sensitive Enterobacteriaceae strains. • Live-cell imaging and fluorescent plasmid