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research focused on biomedical image computing. Our work involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim
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spectrometry; radiopharmaceutical dosimetry with novel agents and/or software techniques; alpha particle RPT radiation biology modeling and analysis of clinical trial data. For additional information on a subset
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for tests, execution of experimental tests, assist in numerical modeling, and development of design equations and guidelines, prepare research reports and journal papers. Organizational Status UBC Smart
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computational models of the immune response for multi-scale epidemic models. This position offers an excellent opportunity for recent graduates interested in applying quantitative and computational methods
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on developing advanced machine learning models to quantify phenotypic traits of crops, including corn, soybean, and other selected species. These models will leverage data collected from various sources, such as
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technologies, drug discovery and advanced therapeutics. We have excellent facilities to support our parallel use of pre-clinical cell and in vivo models, alongside human cellular models, which provide
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(quantitative, qualitative, modelling, GIS, etc.). Produce and share research outputs such as: articles and preprints, demonstrators or prototypes, documented datasets and, where possible, open data, a scientific
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The Department of Biostatistics & Bioinformatics at Duke University invite applications for a postdoctoral associate position in mathematical modeling, as part of a new NIH-funded Center for Excellence in
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patient samples. The Sheffield arm of the project will develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung
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structural data to build 3D models of lectin engagement and immune modulation. The PhD Researcher shall fully devote the whole of his/her professional activity to NOVA.id.FCT, on an exclusive basis, unless it