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The successful candidate will preferably have a PhD in Physics, Chemometrics, Informatics, Chemistry, or related Engineering field and experience in the following skills: • Machine learning and data analysis based
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to extend the contract as part of other grants within the lab. The requirements for the position are: PhD degree in an area pertinent to the project, such as applied mathematics, statistics, machine learning
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plan • Development of machine learning models for the classification of in vivo Raman spectra, applying chemometric methods. • Simulations and modification of data, advanced data analysis • Carrying out
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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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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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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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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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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