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Field
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segmentation methods for clinical applications Surgical video analysis, e.g. workflow recognition, polyp detection, or tissue characterization 3D reconstruction or geometric modelling from medical images
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, through developing predictive models and new experimental methods and instrumentation, to design creative and cost effective CO2 trapping processes. The need is urgent, the task is challenging and a
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the regulation of capsule expression during transition between host compartments. The successful candidate will combine cutting-edge in vitro and in vivo infection models, and both microscopy and flow cytometry
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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with
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models to simulate lifeless and inhabited worlds, and Developing disequilibrium-, redox-, and information-based metrics to understand and quantify the influence of life on planetary environments
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models
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to both fundamental understanding and industrial applications of thermal processing, including product characterization, process design and modeling, and techno-economic evaluation of pyrometallurgical
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and exploit the interrelated processes of viral glycosylation and viral dynamics. Molecular simulations will enable the identification of cryptic pockets in viral proteins that form during the entry
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-on experience in training large neural networks (generative models). Hands-on experience in medical image reconstruction. Preferred Qualifications: Proficiency in Python, C/C++, and Unix-like operating systems
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applications Surgical video analysis, e.g. workflow recognition, polyp detection, or tissue characterization 3D reconstruction or geometric modelling from medical images Translational research in collaboration