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structural modeling, machine learning, and competitive intelligence to infer demand using partial or aggregated signals. While early results are promising, important questions remain: Which partial data is
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-supervised learning, and few/zero-shot techniques — the student will adapt models to ecological data. Bayesian deep learning and ensemble methods will be explored for trustworthy uncertainty estimation
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constrained and valid action space, enabling the generation of reliable answers in a single, efficient inference step. Third, an efficient Data-Driven Low-Rank Adaptation scheme will be employed to efficiently
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
will first exploit parameter-efficient fine-tuning techniques, e.g., pruning and quantisation, to reduce the complexity of LLMs. Then, distributed inference strategies and consensus mechanisms will be
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a VICI NWO grant. Keywords Algorithmic bias, Causal Inference, Discrimination, Fairness, Machine Learning, Targeted Marketing Campaigns Topic The marketing group at Rotterdam School of Management (RSM
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to infer laminar burning velocities by extrapolation) will be quantified by sampling multiple DNS. This work aims to enable the robust inference of kinetic parameters directly from simulations
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work on the following: Design of an efficient foundation model (FM) for generalization across patient anatomies, pathologies, and coil arrangements to infer optimal sensor settings from partial data
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, Quantum Simulation , simulation-based inference High Energy Physics / BSM , Dark Matter , Electroweak Symmetry Breaking , Flavor Physics , Higgs physics , Lattice QCD , LHC , Neutrino , Neutrino physics
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the Universities of Vienna and Innsbruck as well as the IST Austria. Two PhD students will be working on cosmological inference using simulations with Oliver Hahn, and one PhD student will be working with Sylvia
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identifying and analyzing patterns. Indeed, while the success of deep learning on visual data is undeniable, applications are often limited to the supervised learning scenario where the algorithm tries to infer