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application! We invite applications for a fully funded PhD student position to join the research group of Jan Glaubitz to work on Bayesian Computational Mathematics for reliable and trustworthy uncertainty
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, contribute to a better world. We look forward to receiving your application! We invite applications for a fully funded PhD student position to join the research group of Jan Glaubitz to work on Bayesian
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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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datasets from Kepler, TESS, and PLATO to reassess trends in exoplanet occurrence, structure, and evolution. Methods and Tools The project will involve: • Bayesian inference (hierarchical models, posterior
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consideration market benchmarks, if and when appropriate, and internal equity to ensure fair compensation relative to the university’s broader compensation structure. We are committed to offering competitive and
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imaging data. Building on recent critiques of overfitted prediction models, the project will implement a structured, multi-tiered analytical framework, comparing classical regression techniques with modern
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, Ultrasound and Vibration, Aircraft Structures, Damage Assessment, Structural Health Monitoring, Structural Health Prognosis, Bayesian Statistics, Machine Learning Informal enquiries prior to making
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disease, systems biology (including the modeling of signaling pathways and biomolecular structure-function relationships), and stochastic modeling of disease processes such as progression, detection, and
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distribution p(A) is typically incorporated in a Bayesian framework (e.g. enforcing that neighboring pixels are highly correlated). An additional difficulty here is that A is a structured geometric object: an
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suitable evolutionary models Development and implementation of novel phylogenetic approaches, including those implementing protein structural information. Where to apply Website https