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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn't imagine the future, we invent it. If you're passionate about joining a community that challenges the
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
Computational Chemistry, Materials Science, or a related field. Strong background in computational chemistry techniques, including molecular dynamics, quantum mechanical simulations, and machine learning
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, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The successful candidate will design and implement differential privacy
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Home EMA The European Magnetism Association Executive Board General Council Documents Membership EMA news Communication Social Networks Mailing Event Dissemination Rules All news EMA editorials Obituaries Awards beyond EMA Materials 2023 survey Commitments Young EMA EMA Awards Technical...
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Apply now Job no:502733 College / VP Area: College of Engineering Work type:Staff Location:Newark, DE Categories:Research & Laboratory Curious about the full value of working at UD? In addition to salary, our Total Rewards benefits and Compensation Estimator give you a clear view of the...
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a two-year research project in collaboration with international academic partners. The main objective of the project is to study the interaction between machine learning and wireless communication
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] Subject Area: Statistics and Machine Learning Appl Deadline: 2025/12/01 11:59PM (posted 2025/09/04, listed until 2026/02/01) Position Description: Apply Position Description The Department of Mathematics
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At the Technical Faculty of IT and Design of the Department of Sustainability and Planning, Copenhagen, a position as Postdoctoral researcher in Geospatial Machine Learning for Predicting Land Use
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data, including those built from synthetic sources