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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
Description The Center for Interdisciplinary Data Science and AI (CIDSAI) at NYU Abu Dhabi is a cross‑disciplinary hub where mathematicians, computer scientists, engineers, and social scientists
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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | 13 days ago
for Demographic Research, Rostock This network focuses on “scientifically motivated engineering” to design, build, and test digital prototypes—such as machine learning models and large language models—that serve as
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Sensing (DAS) data processing and compression using ML Physics-driven machine learning for geophysical modeling and inversion Thus, the candidate is expected to have or about to have a PhD in a relevant
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory seeks an outstanding postdoctoral researcher to advance data-driven, physics-informed AI for microelectronics materials
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, learning how we think about experiment design, dataset generation, time series analysis, feature engineering, and model building for financial datasets. At Jane Street, our researchers, engineers, and
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the frame of the FNR-CORE supported project OPTMONITOR (Optimal Monitoring and Coupled Modeling for Climate-Driven Landslide Risk Detection) at the University of Luxembourg (Faculty of Science, Technology and
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curious to deliver work that matters, your journey starts here! Innovation. Interdisciplinary collaboration. Complex problem solving. In Carnegie Mellon University’s Department of Mechanical Engineering
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the use of modern machine‑learning methods within applied mathematics—particularly physics‑informed learning, anomaly detection, data‑driven modelling, and the construction of surrogate models grounded in
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 2 months ago
conditions: Scientific area: Bachelor’s Degree in Systems Engineering. Work plan: Model-based digital twins supported by physics-informed machine learning for multiphysics modeling. 1. Grant duration and
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of these systems are insufficient to cover the diversity of human-driven experimental activities. The development of multi-appendage, dexterous robots with embodied intelligence is a key to closing this gap