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Field
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Crick Institute is Europe’s largest biomedical research institute under one roof. Our world-class scientists and staff collaborate on vital research to help prevent, diagnose and treat illnesses such as
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are developing a groundbreaking technique based on autofluorescence (AF) imaging and Raman spectroscopy for detection of positive lymph nodes during breast cancer surgery. The instrument will allow surgeons
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various themes, BC Cancer supports ground breaking cancer research and personalized care approaches through world-class facilities and platforms including genomics, bioinformatics, imaging, drug development
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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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therapies and clinical trials. Your responsibilities will include: processing FASTQ and BAM/CRAM files, and producing variant call files, generating input data for AI tools and implementing secure
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the group's research on developing novel machine learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image
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biological processes by developing theory, innovative modeling tools for large-scale biophysical simulations, and computational frameworks for analyzing increasingly large and complex experimental datasets
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tissue specimens Assemble analysis pipelines using machine learning to process tissue data reproducibly and at scale Conduct analyses using programming languages such as R and Python Collaborate with other
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you will need to evaluate all active thermography approaches for composites inspection, develop a database of raw and processed thermographic images of different defects - geometries on composites, test
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for imaging Apply for this job See advertisement About the position Position as PhD Research Fellow in machine learning available at Department for Informatics with the research group Digital Signal Processing