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for the project implementation. Collaborations among team members will be actively encouraged. The precise scope of the research project of the PhD student will be decided between the appointee and the PI after
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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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Tensorflow.- High level knowledge of fluid mechanics, machine learning and modal decomposition algorithms. - High level knowledge of data analysis algorithms in fluid mechanics. - High level knowledge
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science, machine learning and deep learning to various different data modalities. An ambition of this team is to implement predictive modelling as well as explainable AI methods to understand disease
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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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cutting-edge machine learning techniques to air quality data from a user-centered perspective Communicate scientific results within the Department, in international conferences and write quality papers in
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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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, 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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Experience in large-scale statistical analysis of data Background/experience in building statistical and/or machine learning methods, in particular for data integration tasks, would be a plus Experience in the