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data. You will encode prior knowledge of the collisional processes in tokamak fusion plasmas using sophisticated numeric simulation codes, which will enable you to analyze data from tokamak experiments
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is a lively and research-oriented group of scientists and support staff with a broad interest in information processing in humans and computers, and a particular focus on the signals they exchange, and
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Job Description Are you passionate about environmental contaminants, food safety, marine ecosystems, and creating real-world impact through cutting-edge analytical and data-driven approaches? Do you
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comprehensive training in translational liver research, including data analysis, experimental design, and scientific dissemination. Qualifications We are looking for a highly motivated PhD candidate with a strong
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of demographic methods and experience with software such as R, or similar tools; be eager to learn new demographic methods. Further information about the position can be obtained from Head of Administration Astrid
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assessment. Design and train reinforcement learning agents to optimize operational safety. Build and validate dynamic Bayesian network models integrating empirical and synthetic data. Conduct scenario-based
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: development of automated workflows for multi-omics data generation. In this project you will integrate small-scale automated bioreactors with workflows for the generation of high-quality multi-omics data
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data availability and design parameters. Importantly, the AI implementation should act as a facilitator of creativity, enhancing, and inspiring the early design phase rather than constraining
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dynamic, crowded environments. As a PhD candidate, you will develop methods that combine data-driven autonomy with formal safety guarantees and validate them in real time through simulation and experimental
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; mepage.faculty.ucdavis.edu ). Simonsen will be the main advisor to the PhD student. Her research studies policies that directly or indirectly affect children’s outcomes and she is an expert on the Danish administrative data