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work with Prof. M. Ángeles Serrano and Prof. Marián Boguñá at the interface between Network Science and Machine Learning. The goal is to merge the best of the two worlds to produce a new generation of
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research in Computer Vision and Machine Learning and the potential applications to Biometrics, Explainability, Security, and Media Forensics (among others)? If so, we have the perfect opportunity for you! We
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beyond. To this end, we will use a multidisciplinary approach involving advanced machine learning techniques and top-of-the-line ultra-fast processing platforms to propose an innovative solution that will
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related to modelling (e.g. integrated assessment models, stock–flow consistent models, system dynamics, input–output analysis, econometrics, machine learning, material/energy flow analysis, etc.) Motivation
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Programme? European Union / Next Generation EU Reference Number CEX2021-001202-M Is the Job related to staff position within a Research Infrastructure? No Offer Description Develop and train machine learning
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the top European economics schools. The group has a strong expertise in research areas such as theoretical machine learning, graphical and network models, Bayesian inference, Malliavin calculus, high
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Funded Postdoctoral Position – PERCEPTUAL INFERENCE GROUP - Neural encoding of temporal expectations
research group: Perceptual Inference Group is a freshly established interdisciplinary research group at the BCBL in San Sebastian, Spain. The group combines tools from Machine Learning, Computational
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in Computer, Data Sciences or a bachelor in Biosciences. PhD in bioinformatics, data sciences, machine learning or related areas. Experience: Previous experience on the use of machine learning and data
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fairness, artificial intelligence, machine learning, information retrieval, recommendation systems and/or human-computer interaction. - Data-driven empirical research orientation, including experience in
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. Children at high risk, scoring high in all three dimensions, will be identified from a sex-dependent perspective. Advanced machine learning techniques will be employed to characterize the heterogeneity