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with neuroimaging and neural signal processing tools, including fMRI, structural MRI, diffusion MRI, EEG, or related modalities. Strong publication record in AI, machine learning, computational
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domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School
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Cancer Medicine and the Barts NHS Trust to achieve improvements in cancer patient care. BCI is also a partner in the CRUK City of London Major Centre (together with UCL, Kings and the Francis Crick
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learning to improve detection using multiphase CT imaging and longitudinal biomarker data in routine practice. You will work with multidisciplinary clinical and AI teams on applied research, developing and
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. Possible research topics include: Scientific machine learning Numerical partial differential equations (PDEs) Computational fluid dynamics Neural operators High-performance computing Data-driven
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strategies to port multi-dimensional integrodifferential equations, describing higher order scattering/correlations to specialist large scale GPU hardware. Carry out independent research, work closely with
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Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid
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(adaptive) imaging strategies and reconstruction. The research combines ultrasound physics, signal processing, machine learning, computational imaging, and clinical translation. Beyond your individual
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building and applying state-of-the-art machine learning approaches, including foundation models, variational autoencoders (VAEs), and transformer-based architectures, to integrate single-cell and multi-omic
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19.07.2022, Academic staff The Machine Learning and Information Processing group at TUM works in the intersection of machine learning and signal/information processing with a current focus on deep