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Field
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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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regulatory matters is a great advantage for the position. Important tasks of the work plan • Development of machine learning models for the classification of in vivo Raman spectra, applying chemometric methods
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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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. We seek a computer scientist, biomedical engineer, computer engineer, or related field with advanced programming skills (particularly Python) and experience in Deep Learning frameworks (TensorFlow
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active matter can form bipolar microtubule assemblies able to segregate chromosomes. The project will be co-supervised by Franҫois Nédélec, University of Cambridge, who is a member of the BIOMECANET
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Explainable Machine Learning Research line / Scientific-technical services: Scalable and Explainable Machine Learning TASKS TO BE PERFORMED The purpose of this contract is the collaboration in one of the active
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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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24 May 2024 Job Information Organisation/Company Universitat Oberta de Catalunya Department Research Institute IN3 Research Field Engineering » Computer engineering Engineering » Communication