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Your Job: This PhD project develops a Bayesian inference framework for hybrid model- and data-driven modeling of metabolism, with a particular focus on handling model misspecification. By combining
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and BAT.jl projects. The position also offers opportunities to contribute to research in Bayesian inference and its application to physics in general. The DEMOS project aims to develop state-of-the-art
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development and statistical modelling in resilience assessment (e.g., dynamic/latent-variable models, Bayesian hierarchical models, causal inference, time-series analysis, cognitive modelling) Build robust
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Your Job: This research primarily seeks to incorporate advanced neuron models, such as those capturing dendritic computation and probabilistic Bayesian network behavior, into unconventional
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 2 months ago
field # Knowledge of dynamical systems and network theory # Previous knowledge of pattern formation theory and of Bayesian inference methods is a plus # Previous programming experience in Python is
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analysis Experience in one or more of the following areas is highly desirable: Hybrid modelling, neural differential equations, or physics-informed neural networks Equation discovery Bayesian inference
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processes related to carbon cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset Ability to work independently and cooperatively as part of an interdisciplinary
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be inferred from 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