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Field
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processes that produce energy and raw materials. The Department of Thermodynamics of Actinides is looking for a PhD Student (f/m/d) - Machine Learning for Modelling Complex Geochemical Systems. The job
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into the generation process. This multidisciplinary project will deliver deployable models, reproducible methods, and, where allowed, shareable datasets. The student will gain training in deep learning, AI, image
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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), computation (bioinformatics, machine learning, statistical analysis), working with animals (radio-tracking, animal handling/sampling), and deep knowledge of evolutionary biology and gerontology. The Norwich
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on the performance of the CMF; Using machine-learning (deep learning) methods to develop a predictive model and conduct the sensitivity study to investigate the multiple factors on the performance of flow meter
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Experience and courses in one or more subjects are valued: machine learning, optimisation, linear algebra, deep learning, programming, dynamical systems. Rules governing PhD students are set out in the Higher
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at the intersection of AI and advanced electron microscopy. The project focuses on developing novel self-supervised and physics-informed deep learning methods to restore and denoise Transmission
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frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative–thermal processes at the urban scale. This PhD will develop physics-informed deep learning models for data
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: Master’s degree in a scientific or engineering subject with excellent grades; enthusiasm for neuroscience and brain mapping; openness to new methods such as deep learning. Offer: Funded PhD position (TV-L 13
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. We are working