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increasingly important, but also more complex, due to rising demands on performance, precision, quality, and sustainability. Bayesian optimization (BO) - a special machine learning approach - represents a
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models (POPGN and sparse GPs) for uncertainty quantification of complex process Develop surrogate model for multi-stage manufacturing process and use Bayesian optimization (BO) to optimize the output
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Master Thesis on Bayesian Optimization of Multi-stage Processes with Smart Inducing Point Allocation
models (POPGN and sparse GPs) for uncertainty quantification of complex process Develop surrogate model for multi-stage manufacturing process and use Bayesian optimization (BO) to optimize the output
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strategies. Your tasks in detail: Enhance existing Bayesian state estimation with reliability margins using both simulated and, if necessary, real-world grid data. Develop Use-Case-Specific Reinforcement
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and reduction Development and application of big data analytics for large X-ray data sets Application of Bayesian methods to X-ray data Combinatorial analysis of various data from complementary
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Bayesian belief networks; Experience in scenario development approaches, e.g. SSPs; Experience in the application of R-based analytical tools for qualitative or semi-quantitative modelling, incl. RQDA
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sampling algorithms to Bayesian learning paradigm Quantum-assisted training algorithms for sparse machine learning models. What you bring to the table Formal conditions to start a master thesis on a German
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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
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candidate will show in-depth methodological and applied knowledge in the field of machine learning, especially deep learning, experiences in the area of uncertainty quantification, generative and Bayesian