73 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "NORTHUMBRIA UNIVERSITY" positions at UNIVERSITY OF HELSINKI in Finland
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machine learning. We focus on inductive logic programming (ILP), which learns logical rules from data. We primarily use automated reasoning techniques, such as SAT/ASP/SMT/MaxSAT solvers, to learn rules
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machine learning. We focus on inductive logic programming (ILP), which learns logical rules from data. We primarily use automated reasoning techniques, such as SAT/ASP/SMT/MaxSAT solvers, to learn rules
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and machine learning. We focus on inductive logic programming (ILP), a form of inductive program synthesis which learns logical rules from data. The focus of this position is to develop ILP/program
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intelligence, big data frameworks, bioinformatics, data analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and
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, automation, information systems, and machine intelligence represent technological methods that are important for future in order to develop environmentally sustainable farming processes, improve energy and
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facilities, and opportunities for professional development (https://www.helsinki.fi/en/about-us/careers ). YOUR PROFILE PhD in biology, mathematics, or a related field Strong background in mathematical
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artificial intelligence/geospatial AI, methods of machine learning and deep learning development of computer vision applications and image recognition methods analysis and production of big data, including
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University. Would you like to know more about the University of Helsinki as an employer and Faculty of Biological and Environmental Sciences Departments? Learn more: https://www.helsinki.fi/en/about-us/careers
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teach German language and culture to students who have no previous knowledge of the subject. The position is part of the national SARAVE project of Finnish universities and located at the University
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-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning