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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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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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programming Expertise in additional quantitative research methods (e.g. time-use analysis, system dynamics, machine learning, econometrics, advanced statistics, big data, material flows analysis, etc
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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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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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, the position is most appropriate for recent master's graduates (or soon to graduate) in fields related to machine learning, computer science, material science or related disciplines with excellent academic
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to machine learning models • Simulations and modification of data • Carrying out Raman measurements in vitro and in vivo • Development of protocols for biocompatibility and strict compliance with regulations
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seeking candidates with a PhD degree and expertise in an area pertinent to the project and experience in: Machine/deep learning algorithms Biomedical informatics Computer Science Expertise
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from in vivo Raman spectra • Applying chemometrics methods to machine learning models • Simulations and modification of data • Carrying out Raman measurements in vitro and in vivo • Development
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, provisioning systems, input–output analysis, system dynamics, machine learning, and/or material flow analysis is desirable. Experience with existing ecological macroeconomic models (e.g. PyMedeas, EuroGreen