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related to modeling and simulation of biological systems, 3) very good IT skills, in particular the ability to program in Python, 4) very good knowledge of machine learning methods, neural networks, and
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meaningful explanations when unexpected situations arise. We plan to represent and model expectations using formal methods, develop techniques that help a system reason about human expectations at runtime, and
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University Lisbon, for the development of teaching and research activities in the disciplinary field of Humanities, namely in the fields of Digital Humanities and Quantitative Methods. The teaching and
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 11 hours ago
. Description: The relatively recent subject of Astrobiology - the study of the origin and prevalence of life in the universe - only formally launched in the late 1990s, is an exciting and rapidly growing field
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of microfluidics in the aforementioned demanding fields. This project aims to find methods to eliminate these urgent packaging issues. As the demand for advanced ventilators and multi-infusion systems grows, finding
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diagrams and procedures, identify and manage project issues, risks, and decisions, and develop progress dashboards, reports, and other methods to communicate project progression and ensure timely project
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and automata, and the interaction between formal methods and artificial intelligence. We welcome candidates interested in topics ranging from foundational theory to the development and evaluation
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candidate will use programming and computational tools (including Python, MATLAB, and related libraries) to develop, test, and evaluate machine learning models; review, proofread, and execute formal
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and perform laboratory procedures and testing as needed to support patient care and operational needs. Ensure laboratory testing and treatment methods are scientifically valid, clinically relevant, and
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are looking for a motivated and talented PhD candidate to join a unique interdisciplinary project at the intersection of machine learning and formal methods. Information Machine learning models deployed in real