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comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures ( https://CGRlab.github.io/research/ ). About the research project We are open
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eSSENCE graduate school in data-intensive science. The school addresses the challenge of data-intensive science both from the foundational methodological perspective and from the perspective of data-driven
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description Quantitative analysis of lipid nanoparticles (LNPs) using Cryo EM is challenging due to heterogenous samples, lack of training data and sample variability. In this project we aim to develop AI
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group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification in large pre-trained models (vision-language models), generative models (flow
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develop methods within one, or multiple, of the following categories: Optical engineering, fluorescence microscopy, image analysis: Development of microscopes, fluidics, and data
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and the SciLifeLab. If you wish to know more about our research, team, or professional values, please visit: https://liu.se/en/research/cantu-lab and https://www.scilifelab.se/researchers/claudio