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Field
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datasets with machine learning methods, and software development are beneficial Good organisational skills and ability to work systematically, independently and collaboratively Effective communication skills
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platform. Where to apply Website https://fenix.tecnico.ulisboa.pt/fenixedu-admissions Requirements Research FieldEngineering » Computer engineeringEducation LevelUndergraduate Skills/Qualifications Admission
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, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines Language requirements English C1 or equivalent Application deadline January, please see website for exact
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science (for example, but not limited to, data science, machine learning, statistics, computer science, mathematics, etc.) acceptance by a PhD supervisor with examination admission for PDS and formation
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the complex multiscale nonlinear interactions at the origin of such extreme events. In this project, you will develop machine learning-based reduced-order models which can accurately forecast
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growth methodology based on real-time growth monitoring enabled by advanced in situ characterization tools (RHEED, ellipsometry, curvature measurements, flux monitoring), coupled with machine-learning (ML
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analysis Background in biomedicine and digital pathology What we offer Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within
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of machine learning and deep learning methods and classification of health and wellness parameters. Data acquisition, as well as the preparation of presentations, scientific publications, and technical reports
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skills, including proficiency in statistics, scientific programming, and/or modelling. We especially welcome candidates interested in applying AI and machine learning to analyse heritage datasets
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statistics, scientific programming, and/or modelling. We especially welcome candidates interested in applying AI and machine learning to analyse heritage datasets. What we offer: • A stimulating