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through the atmosphere. These models will be used, in Bayesian inference frameworks, to estimate surface fluxes from in situ and satellite observations. The derived emissions are used to track progress
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the quality of uncertainty estimates by standard methods for LLMs, particularly with deep generative models, and (iii) develop a benchmark for uncertainty quantification in LLM-based scientific agents. The main
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, Bayesian inference, and decoding/encoding methods. (Optional) Conduct in vivo calcium imaging experiments in C. elegans to study how neural circuits generate behavior. Engage in creative, hypothesis-driven
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that both parameter estimation and model selection can be interpreted as problems of data compression. The principle is simple: if we can compress data, we have learned something about its underlying
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, custom-trained neural networks, and related tools. Analyze and interpret high-dimensional neural datasets using systems neuroscience approaches such as neural networks, Bayesian inference, and decoding
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multi-dimensional niche models, and applying advanced Bayesian spatio-temporal methods. You will: Build n-dimensional abiotic niches for >6,700 species and estimate population positions within them
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of the research include: (1) Designing and executing methods to integrate data from different sources, including developing a Bayesian Hierarchical Modeling framework; (2) using integrative modeling approaches
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-ever Immune Digital Twin – a personalizable computer replica of the immune system – to enable everyone and anyone to assess and optimize the health of their immune system and simulate and predict its
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” between data and models, including likelihood-free inference (e.g. Approximate Bayesian Computation) and simulationbased calibration, to ensure the ABMs remain predictive and falsifiable rather than
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personalizable computer replica of the immune system – to enable everyone and anyone to assess and optimize the health of their immune system and simulate and predict its future ability to respond to diseases. Why