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Field
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image sequences. As a benchmark, end-to-end deep learning models will be developed using raw image data. In parallel, shallow learning models (e.g., Gaussian processes) will be explored based on insights
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, or related Experience and skills · Multi- and hyperspectral images processing · Knowledge of quantitative remote sensing · Knowledge of physical and statistical modelling concepts
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of porous electrodes, advanced imaging and characterization, and validation in electrochemical cells. Marie Skłodowska-Curie Doctoral Networks are joint research and training projects funded by
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to the problem of thermal measurement at the nanoscale. This thesis is part of the ANTICHI (Advanced Nanoscale Thermal Imaging and CHaracterization Instruments) project, which aims to provide a versatile
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domains are e.g., signal-/image processing, artificial intelligence and machine learning. Tasks: research and development in designing and programming field programmable gate arrays (FPGAs) for accelerating
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-performance computing and imaging facilities. a collegial, international atmosphere and flexible, family-friendly working hours a structured PhD programme with extensive training and career-development
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diagnostics (theranostics), focused ultrasound (FUS), and molecular imaging (PET/SPECT). The PhD candidate will be responsible for conducting preclinical studies using animal models of glioblastoma, including
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laboratory and imaging facilities Access to a full range of equipment and laboratory facilities Inclusive and supportive lab culture with flexible working practices Applications welcome from candidates from
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-critical decisions in real time. These systems rely heavily on sensor data (e.g., GPS, pressure transducers, image processors), making them vulnerable to stealthy threats like False Data Injection (FDI) and
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focus on solid dosage forms and 3D printed drug products. The research will integrate advanced imaging, computational modelling, and pharmaceutical sciences to improve the resolution, reproducibility, and