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tissues and (where relevant) soil. The modelling work will be tightly coupled to experimental campaigns and will use image-derived structures (including XCT images where available) to develop mechanistic
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Antibiotic testing is still defined by bulk growth inhibition measurements. We aim to replace this paradigm by quantifying antibiotic action at single-cell resolution. The scientific aim Our
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segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI, and research team to ensure timely achievement of project deliverables. Undertake the following
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About us The Division of Biosciences at UCL is one of the world's foremost centres for research and teaching in the biological sciences. We have an outstanding international reputation and a
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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow in Machine Learning for Computational Pathology, Medical Imaging, and Clinical Text Analysis Department Bashashati
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 6 days ago
Engineering, Computer Science, or Bioengineering. Knowledge of image processing and image analysis, preferably applied to medical imaging (e.g., segmentation, feature extraction, geometric reconstruction
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tissues and (where relevant) soil. The modelling work will be tightly coupled to experimental campaigns and will use image-derived structures (including XCT images where available) to develop mechanistic
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Experience with image-based modelling workflows (CT/XCT segmentation-to-mesh pipelines). Experience with inverse problems, uncertainty quantification, or modelling of biological tissues/porous media. Informal
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TypePermanentWorking TypeOn siteAvailable for SecondmentNoClosing Date27-Apr-2026JD_Param11YesJD_Param12Yes The Division of Biosciences at UCL is one of the world's foremost centres for research and teaching in
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zu Berlin contributes to the development of NOMAD, the world’s largest data repository for materials science. Within SolMates, the team builds on its experience in integrating image segmentation and