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Field
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exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised
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. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set
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. The platform enables automated behavioral analysis and supports researchers with valuable insights. What you will do Develop and maintain machine learning inference pipelines using Python and Docker Support
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Lösungen für inverse Probleme in der diagnostischen Biomechanik beizutragen, mit besonderem Schwerpunkt auf Elastographie. Das Projekt baut auf unserem kürzlich entwickelten Weak Neural Variational Inference
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software (e.g., LLM agents for finding and fixing bugs) Static and dynamic program analysis (e.g., to infer specifications) Test input generation (e.g., to compare the behavior of old and new code via
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to avoid sources of bias such as the target trial approach (www.bips-institut.de/en/research/cross-departmental-working-groups/working-group-gepard-target-trials-for-causal-inference-gettcausal.html ) and
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), mathematical evolutionary modeling (game theory, dynamical systems, agent-based simulations or other), bespoke probabilistic modeling / (Bayesian) data analysis (e.g., in the Rational Speech Act framework
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resides in is to build up technology for data-driven pattern recognition to infer data-driven generalized auditory profiles. The central challenge for this endeavor are the small sample sizes (of typically
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influenced corrosion (MIC) in marine environments. It uses AI-supported models, Bayesian data fusion, and real-time sensor data integration. Your responsibilities include: Development of a digital twin (DT
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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