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, stochastic thermodynamics, and quantum physics. The research will focus on three main directions: Thermodynamic computing: developing physics-inspired alternative models of computation that aim to reduce
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arrays) from experimental data, leveraging training on simulated datasets. Interpretable neural networks for physics: Development of interpretable deep learning models for identification of matter phases
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qualifications, experience and knowledge Knowledge of the physics of active galactic nuclei, experience in the analysis of observational data for active galaxies (spectral decomposition, time-delay modelling
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