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collaborators. Your work will develop algorithms, inference methods, and frameworks to adapt models from training data to test environments, which is necessary to resolve distribution shifts, hidden confounders
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Science and Engineering, or a related area is required. The position will involve developing models and algorithms for the evolution of inorganic aerosols in the atmosphere, building upon the research
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advantage through coherence-driven materials design and realizing scalable, modular quantum systems. These efforts integrate vertical co-design, from materials to algorithms, and cross-platform co-design
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network modeling to address real-world policy challenges through algorithm development and technical analysis. Key Responsibilities Conduct original research in generative AI Train and supervise graduate
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/or observationally motivated model-building for SMBH feeding and feedback. The group integrates observations, theory, data analysis, algorithm development, model-building, and numerical simulations
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and frameworks we work on, and opportunities for applying the methods with top-notch collaborators. Your work will develop algorithms, inference methods, and frameworks to adapt models from training
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of machine learning (ML) and quantum many-body physics. We are also happy to work with experts in one of the two fields who are committed to learning the other. Moreover, we look for interest in developing
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project from the Dutch Polymer Institute (DPI). The main objective is to develop and validate a real-time, AI-enabled multi-sensor fusion platform for diagnostics and prognostics of polymeric bearing cages
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concerned with optimal transport for inverse problems. Optimal transport for inverse problems One of the central topics of the research projects is the further development of theory and methods
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that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In this role, you will have the opportunity to lead and contribute