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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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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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candidate is expected to hold: A master degree in biomedical engineering or computer science, Excellent programming skills (Python). Experience with data curation, large-scale datasets, and machine learning
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properties through computer simulations. The project also includes validation of materials in the lab and translating the findings into practical recommendations for use in real power-electronics environments
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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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are more than welcome to contact us. You will find contact persons at the bottom of the jobpost. Further information Read more about our recruitment process here. The assessment of candidates
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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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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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your motivated application, CV and exam certificates. If you would like additional information about the position, please contact Academic Employee Yaman Göksel at yagok@dtu.dk , or Professor Anja Boisen
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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