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Field
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advanced imaging to study the deformation process of flexible biomaterials due to the effect of cellular forces. 6. Applicable legislation and regulations: Research Fellow Statute, as amended by Decree-Law
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* The successful candidate will work on projects performing analysis of PET neuroimage data, including kinetic modeling and multimodal data synthesis. Responsibilities will include image data processing and analysis
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Postdoctoral Research Fellow in Multimodal Foundation Models and Biomedical AI – AI/CS-Oriented Role
across visual, textual, and biomedical data modalities. While one major application area is biomedical and neuroscience imaging, the research will emphasize core AI challenges in representation learning
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interaction and behavioural observation, therapeutic intervention and psychopharmacology, and child/family studies. Facilities include functional magnetic resonance imaging (fMRI), electroencephalography (EEG
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bronchoscopy programmes for patients with premalignant changes in their lung. We offer a rapid diagnostics clinic in the UCLH Macmillan Cancer Centre, which includes state-of-the-art imaging with computed
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. Responsibilities Perform intravital surgery to implant optical imaging windows over draining lymph nodes for long-term, real-time tracking of T-B cell interactions. Develop novel sensors for intravital imaging using
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Utilising our world class facilities • multiple omics • cellular and sub-cellular imaging • biophysics • structural biology - cryo-EM • bioinformatics/AI • CL3 facilities All
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hospital intensive care unit. The post holder will have primary responsibility for the development and evaluation of the system, and of the data processing and image display software. The post holder will
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, including design approaches, scanning methods, signal processing techniques, and comparison with alternative detection technologies. ii. Support design and development of NQR prototype, including system
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems