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Field
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intelligent, agentic networks—combining causal inference, conformal prediction, and agent-environment modeling—to ensure trustworthy decision support and debugging of autonomous control systems? The PhD will
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, the candidate will help the team on the following tasks and research areas : - improve integration of the framework with learning/inference framework (PyTorch, Keras, AIDGE, ...) - improve implementations of low
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modelling: -Weighted PINNs, -Bayesian PINNs, -Stochastic PINNs, -Ensemble PINNs, -Domain-decomposition PINNs. Selected approaches will be tested within a dedicated data-assimilation framework
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representation of streamlines inferred from geomagnetic data, from Finlay et al. Nat. Rev. Earth Environ., 4:377–392, 2023 and numerical simulations by Schaeffer et al. Geophys. J. Int., 211(1):1–29, 2017
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-of-the-art multimodal neonatal and adult MRI data. Computational models allow the inference of whole-brain neuronal dynamic characteristics underlying the association between local functional dynamics and
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the evolution of massive binary stars into compact binaries as sources of gravitational-waves and astrophysical inference on gravitational-wave observations. My research group on massive binary evolution -- also
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analyses is beneficial Expertise in machine learning and Bayesian statistics is beneficial What we offer: Work on a scientifically exciting, socially highly relevant, and globally visible ERC Synergy Grant
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strong background in statistical inference Experience with the validation of Earth Observation products Strong programming skills, preferably in R/Python or similar. Excellent English communication skills
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complemented with a supersonic module grounded on the work of Bufi & Cinnella (link to the research paper). In a second step, using Bayesian processes (Lam et al., 2015) and new acquisition functions
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scientific discoveries explore and evaluate usability of system architectures such as Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference implementation of your machine learning