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theoretical and computer modelling. A performant model will allow a more realistic simulation of electrophoretic systems, widening our understanding of the phenomena and enabling the improvement of current
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: structuring and processing the results obtained. Statistical knowledge is required (data mining, hierarchical cluster analyses, survival analysis, machine learning). Job profile You hold a master's degree
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behaviours of many species systems as a consequence of the interactions between the individual species (agents). Currently, these methods are more and more combined with machine learning techniques. BIONAMIX
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research tasks include: Developing and optimizing UHPLC-HRMS methods Conducting metabolomics analyses on stool and saliva samples Processing metabolomics data using multivariate statistics and machine
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(not agronomy), or Biosystems Engineering Basic knowledge in sensing technologies and measurement systems. Basic knowledge in machine learning, deep learning and/or data fusion and modelling tools, and
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(bachelor + master) in the European Union. You have a strong background in machine learning, including Natural Language Processing. You have excellent coding skills; hands-on experience in deep learning
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of studies (bachelor + master) in the European Union. You have a strong background in machine learning, including Natural Language Processing. You have excellent coding skills; hands-on experience in deep
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signalling towards personalized medicine, using statistics, bioinformatics and machine learning, as well as high-throughput biology in the framework of gene regulatory networks. Our final goal is to use
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fundamental and applied research in the domain of language and translation technology and has extensive expertise in the use of machine learning for a wide range of language technology problems, int.al
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Machine Learning Potential (eMLP), which reconciles these requirements in a unique way: it is not only fast and accurate, but also able to describe physical and chemical phenomena inaccessible to analogous