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expertise in quantitative biophysics and/or modeling approaches. • Experience in advanced imaging, quantitative data analysis, and image processing. • Ability to develop interdisciplinary approaches
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organization, and how these properties are altered by pathological processes across multiple biological scales. The position is available in the team of Dr. Andra C. Dumitru in the nBIO group at LIBST, UCLouvain
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the excellence cluster Your profile PhD in physics or in a related discipline experience in phase contrast or coherent X-ray imaging advanced programming skills in a high-level computer language (Python or related
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the clinic and in silico. We focus on neurodegenerative processes and are especially interested in Alzheimer's and Parkinson's disease and their contributing factors. The LCSB recruits talented scientists from
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origins (bacterial, biological fluids, hybrid nanovectors, etc.). He/she will develop protocols for fluorescent labeling (e.g. DNA paint...), immunolabeling, immunoimmobilization, acquisition, processing
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, bioinformatics, production of genetically engineered animal models, single-cell profiling, and advanced image processing and analysis. In addition, numerous institutional shared resources and core facilities
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configurations hinders the creation of generalizable solutions for processing these images. This project proposes an innovative approach that combines state-of-the-art diffusion models with physical radar
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CANDIDATES ONLY About Us The applicant will join the Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. We are a highly collaborative
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School of Biomedical Engineering & Imaging Sciences has attracted multi-million pound research funding and collaborates with other universities and Faculties within KCL, the NHS, charitable trusts and
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fundamental radar wave propagation equations into the diffusion process, allowing for more accurate and physically consistent image restoration. The developed framework will be able to transfer knowledge