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actively encouraged. The precise scope of the research project of the PhD student will be decided between the appointee and the PI after the candidate has been appointed. The appointee should have
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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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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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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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plus Experience in the development and/or implementation of algorithms and/or computational pipelines Background/experience in building statistical and/or machine learning methods, in particular for data
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experience in building and interacting with relational databases (e.g., PostgreSQL) and APIs would be a plus Education and training PhD in Bioinformatics or in Biology, Machine Learning, Statistics, Physics
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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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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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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