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of numerically stable and efficient computational algorithms, their implementation and testing with computer programs, both in simulation as well as real robotic systems. Information about the project and the
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and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry. Subject description Machine learning focuses on computational methods by which computer
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of pesticide exposure on non-target organisms, such as pollinating insects. The work includes toxicity studies of individual and combined pesticides, encompassing planning, method development, rearing
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Machine learning focuses on computational methods by which computer systems uses data to improve their own performance, understanding, and to make accurate predictions and has a close connection to
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you in interested developing new machine learning methods
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This is a broad call for five fully-funded PhD positions in computer science and engineering to work on machine learning, autonomous systems, software engineering, formal methods, and network
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computer networking; experience with algorithms for categorisation within large data sets. Some familiarity with concepts and methods from life cycle assessment, and propensity to contribute to simple
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collaborative research environment with high scientific standards. About the research project This PhD research project is focused on developing theoretical and computational methods for analyzing strong force
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methods to unravel the gene regulatory mechanisms underlying fundamental biological processes. We aim to understand how these processes are disrupted in various diseases. Our extensive collaborations with
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develop new theory and methods for the control and estimation of large systems, using tools from optimal transport theory. Optimal transport is a mathematically rich theory for comparing probability