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, reachability approaches or Bayesian filtering and develop a robust prediction algorithm. These dynamic predictions must then be integrated into a map representation to be included and used in the path planning
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can be filtered to support this, but in many areas truth is not established. Many text sources are opinions, may contain argumentation and indeed subtle or not so subtle propaganda, written in many
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of Bayesian approaches such as Gaussian process regression, particle filters, Bayesian networks, graph-based approaches. Probabilistic -based uncertainty quantification is also essential. Support the design
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uncertainties. Knowledge of Bayesian approaches such as Gaussian process regression, particle filters, Bayesian networks, graph-based approaches. Probabilistic -based uncertainty quantification is also essential
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cleaning, filtering, etc.).•Expertise in data fusion and relevant algorithms (deep learning, generative AI, kernel methods, Bayesian methods). •Preferably, experience with high-content imaging or cell
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methodologies capable of operating under severe distribution drift. By decoupling OOD filtering from informativeness scoring and leveraging adaptive, drift-aware querying strategies, this research aims