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Field
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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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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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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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with applications to aerodynamic and mechanical databases. - POD, DMD, sPOD, hoDMD, auto-encoders and other machine learning techniques will be used. - Software development. - Writing of technical
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C++, Pytorch Al menos 3 años de experiencia en Visión por Computador y en Machine Learning/At least 3 years of experience in Computer Vision and in Machine Learning Specific Requirements Al menos 3
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fields: - Design and implementation of AI algorithms (split learning) for collaborative and decentralized learning environments. - Design and implementation of appropriate mechanisms to secure
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fields: - Design and implementation of AI algorithms (split learning) for collaborative and decentralized learning environments. - Design and implementation of appropriate mechanisms to secure
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learning tools applied to identify patterns in complex flows. Accelerate CFD codes in fluid mechanics problems. Requirements Research FieldEngineering » OtherEducation LevelBachelor Degree or equivalent
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Intelligence (AI) methods and algorithms in e-learning platforms, especially in the implementation of content recommendation systems in moodle. Development of data science and text mining projects. Requirements
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career and aspire to a professorship at POLYMAT. EMAKIKER grant should enable female researchers to acquire independence and scientific autonomy and to sharpen their profile for the next career step