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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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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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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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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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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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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