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store any personal data. Functional Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party
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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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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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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