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, active learning, Bayesian optimization, agentic AI, or closed-loop materials discovery. Experience in computational heterogeneous catalysis, electrocatalysis, surface science, electronic-structure analysis
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and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured biological data are increasingly common in modern
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models that are incomplete and data that involve errors. For such challenges, Bayesian analysis using Markov Chain Monte Carlo (MCMC) has become the gold standard. For addressing high dimensional parameter
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generalized estimating equations in R (lme4, nlme, glmmTMB). (EF) Model latent variables, structural equations, and longitudinal growth patterns in Mplus or R. Diagnose missing data mechanisms and apply modern
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. • Research expertise in artificial intelligence (AI), causal machine learning, computational modelling, and single-cell multi-omics. • Experience modelling mechanisms of genome structural dynamics in clinical
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materials to improve catalytic performance in water oxidation reactions). To achieve this goal, we will leverage BCAM’s enhanced Bayesian sampling techniques, generalized hybrid Monte Carlo (MC) schemes, and
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radii and tidal deformabilities, connecting nuclear-structure experiments with astrophysical observations within a common statistical framework. By combining relativistic nuclear theory, Bayesian
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Several explanations for hallucination exist, but perhaps the main reason is that they are trained to be plausible. There is no element of truth-seeking in their construction. The training content
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. This should include expertise in linear-mixed and structural-equation frameworks. Expertise beyond those domains is very welcome, including Bayesian and machine-learning. Preference will be for individuals who
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a small fraction of the sample voxels need to be re-measured to detect the change, rather than the complete 3D volume. Microchip samples are also highly structured, with known design rules, which can