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that lower the carbon and computational footprint of training and inference. Parameter-efficient fine-tuning: Harnessing large foundational vision–language models using adapters, LoRA, low-rank updates, and
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Skłodowska-Curie (MSCA) funded European doctoral network that unites leading institutions to train a new generation of researchers in health economics, causal inference, and the analysis of large-scale
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to estimate both contemporary and past effective population sizes. With these in hands, the project aims to verify how well reported dynamics of decline in rare plant species align with inferred trajectories
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inference, i.e. semiparametric or nonparametric methods, for directional data. Candidates must hold a Master degree in Statistics, Mathematics, Applied Mathematics, Economics, Business Engineering, Civil
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science, mathematical statistics, actuarial science, public health, or psychology – who have strong quantitative skills and an interest in causal inference. PhD Program The project is based in the Centre
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framework that compares and blends complementary paradigms of physics informed machine learning (such as PINNs, ODIL)—to (i) super-resolve experimental data, (ii) infer unknown parameters such as the
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preliminary receiver positions. Despite these advances, current literature provides little guidance on how to systematically incorporate domain-specific constraints into the training and inference
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research on the development of new inference methods and algorithms for wide classes of stochastic models. However, research will be conducted in collaboration with biologically oriented researches allowing
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tracking and autoencoders for amplitude and phase noise characterization Bayesian filtering Building experimental set-ups for noise characterization Reinforcement learning strategies for comb generation
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of Mathematics at Radboud University (Nijmegen, Netherlands), and join the research group of Laura Scarabosio, funded by the NWO Vidi programme ’Taming Frequency in Bayesian Inverse Wave Scattering’. Inverse wave