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based at CIMAT and UNAM, and within the project “Physics informed surrogate models for Bayesian Uncertainty Quantification (BUQ)” are seeking two postdocs. The position will involve basic research
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Learning for Biomedical Data. The postholders will focus on developing and applying state-of-the-art generative models (such as VAEs, GANs, and transformer-based architectures) to large-scale biomedical
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modification. By marrying first-principles theory – trade-off analysis, game theory, reaction-diffusion, and consumer-resources models – with single-cell tracking and synthetic community experiments
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) analysis Mathematical modelling Microbiology Molecular biology We approach science with the view of physics, aiming to identify principles behind complex systems. We collaborate closely with engineers
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of the national public health, during the construction of a core base for building a national medical science and technology innovation system, we focus on the research and development (R&D) of innovative vaccines