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heating and cooling, storage, and local electricity grids. A key goal is to translate methodological innovations in deep learning into practical tools for sustainable urban energy systems, supporting
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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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key to revealing how the brain tracks, processes, and learns auditory information. Methodologically, the project will explore the integration of information-theoretic decompositions, deep neural
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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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learning, image analysis, and advanced computing to study relationships between structure and function. Keywords: Human Brain, 3D Atlas, Deep Learning, Temporal Lobe, Brain Function Entry Requirements
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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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of the bacterial chromosome structure. Technical building blocks could include state-space models, generative models in the form of diffusion models, deep learning, optimal transport, and probabilistic modelling in
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deep learning environments (e.g. pytorch) or a computer systems background CDT studentships The CDT has a minimum of 10 fully-funded studentships available for September 2026 entry. Studentships include
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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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in ML research. Demonstrated ability to work on the interdisciplinary team. Strong programming skills and familiarity with deep learning libraries. Empty heading Preferred Knowledge, Skills, and