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, b) computational modelling, or c) machine learning. This is a complex project managing longitudinal data from a range of sources. Therefore, thoroughness and attention to detail while managing
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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genome-resolved multi-Omics methods, statistical/metabolic modeling, and machine learning. The postdoc will apply these approaches to generate a systems-level understanding of microbiomes including
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of solar energy systems, applying machine learning methods, and data analysis. A background can be in materials engineering, physics, electrical engineering, computer science, or another suitable field. In
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. This recruitment is funded as part of the implementation of a Prematuration Programme by the Alliance Sorbonne Université University Innovation Cluster (PUI-ASU). Where to apply Website https://utc.recruitee.com/o
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materials and technologies. Using advanced computational modeling and machine learning, we seek to elucidate the mechanisms governing the self-assembly of lignin in different solvents and the formation
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collaborates with faculty, students, and external partners to translate rigorous methods into practical tools. QUALIFICATIONS: Ph.D. in Applied Mathematics, Electrical & Computer Engineering, Computer
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Location(s) Number of offers available1Company/InstituteFaculty of Computer ScienceCountrySpainGeofield Contact State/Province A Coruña City A Coruña Website https://www.udc.es/ Street A Maestranza 9 Postal
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computer scientist with experience in bioinformatics, solid programming skills and knowledge in 3D protein structures. Machine learning skills and knowledge of Web development are a plus. Good interpersonal
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, Neuroscience, or a related field by the start date. Demonstrated expertise in computational modeling of human behavior or computer vision / machine learning. Proficiency in Python, MATLAB, or R. Strong