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). The candidate should have hands-on experience developing state-of-the-art machine learning models, particularly deep neural networks (experience with graph neural networks is highly valued). Their background
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and improve computational methods for the pre-processing of MS data, exploring new algorithmic approaches for signal detection, deconvolution, and feature extraction. Machine Learning for Chemical
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for the research topic “Modelling the impacts of soil pollution on soil ecosystem services”. The topic is focused on ecosystem services, soil ecotoxicology, soil contamination, and ecological risk assessment. The
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of the research activity involves independent and collaborative research within the field of L2/Ln acquisition and learning Contribution to sub-group “Input and Frequency Effects in L2 Acquisition” within research
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. • Independence in learning and working, with documented research productivity. Priority will be given to candidates who have co-authored at least one scientific publication (submitted or accepted). Experience with
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service At least one scholarly publication in the last 3 years listed in the Thomson Reuters Web of Science or Scopus database, and of the type “article,” “book,” “book chapter,” “letter,” or “review” (as