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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
from all backgrounds to join our community. The Nonlinear Systems and Control group is seeking a talented and ambitious Postdoctoral Researcher to develop machine learning-enabled approaches
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. The position bridges machine learning and molecular science, with opportunities for collaboration, mentorship, and impactful research. About us The Department of Computer Science and Engineering (CSE
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an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology Machine Learning, AI Safety, AI Alignment, Eval of LLMs, Multi-agent Safety
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of computational mechanics, mechanical and civil engineering, and scientific machine learning. The postdoc will be encouraged to publish in leading international journals, present their work at major conferences
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around 200 dedicated staff, including 65 PhD students . We are a joint department between Chalmers University of Technology and the University of Gothenburg, combining the best of two academic worlds in
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, development of chemical process solutions for repurposing of electrodes, and integration of AI-based vision and active machine learning to optimize the efficiency of the process. Writing publications and
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collaborators. Qualifications Applicants must hold a PhD degree in electrical/electronics engineering, telecommunications or related field. Other requirements include: Strong background in communication theory
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machine learning technologies in order to provide evidence-based decision support tools in near real time across a variety of thematic domains: disaster risk reduction, sustainable agri-food systems
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inverters to enhance grid flexibility, reliability and stability. • Apply machine learning and AI tools for the battery system health estimation and maintenance prediction and integrate analytics
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models