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development of a novel generative AI framework for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein
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application of advanced machine learning techniques, including deep learning approaches, aimed at identifying complex patterns of host immune responses to vaccination. The research activities will also include
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conditions (Burgard et al., 2022). The application of deep learning to this problem has yielded promising results (Rosier et al., 2023; Burgard et al., 2023). Further development and refinement
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project at the forefront of microscopy and bioimage analysis (https://www.msca-agile.eu/ ) and contribute to OMERO integration and optimization of Biom3d, a cutting-edge deep-learning framework for 3D image
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. Expertise and knowledge in AI deep learning model development on histology whole slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and
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do things, especially considering recent advancements in AI technology. The position will include developing radiomics and deep learning models from contrast-enhanced computed tomography images
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segmentation, target detection and change detection along and across multiannual series of data. Methodologies like foundational models, machine learning, deep learning, multitask learning, enforcement learning
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, and synthesis using established tools and deep learning frameworks. The postholder will work within the School of Medicine and Population Health under the supervision of Dr Bilal Tahir, collaborating
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will design and validate advanced multi-agent Deep Reinforcement Learning (DRL) and/or Digital Twin (DT)-enabled methods for efficient, scalable and time-critical handover optimisation. The work will
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project description The research subject is applied mathematics. The project focuses on computer vision and machine learning, with a particular emphasis on deep learning methods for medical image analysis