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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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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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, 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
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for anonymization and preparation of medical data for analysis, training, deployment and monitoring of novel machine learning-based tools on remote devices and providing support, in general, for the software
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PhD degree and expertise in an area pertinent to the project and experience in: ‐ Machine/deep learning algorithms ‐ Biomedical informatics ‐ Computer Science - Expertise on the implementation and
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++, Python and/or Matlab. Experience in Rendering techniques and/or Machine Learning is highly valued. Experience with Monte Carlo methods is also a plus. Work Environment The work will be developed within