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Field
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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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project is to develop a high-performance computing framework for mass spectrometry proteomics to enhance efficient processing and interpretation of large datasets using deep learning algorithms and GPU
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. university groups within CMS. Our instrumentation expertise spans detector operations and upgrades (Level‑1 muon trigger, CSC muon system), tracking algorithm R&D, and future HL‑LHC trigger algorithm
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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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learning algorithms into professional software with an intuitive user interface, incorporating feedback from CHWs through iterative design and evaluation cycles. The selected candidate will be part of a
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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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development of detectors sensitive to ultra-high dose rates. Developments of computation methods for small-field dosimetry, radiation detection systems used in photon, proton, and electron beam radiation
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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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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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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