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Computational Mechanics. Solid background in continuum mechanics and numerical modeling Strong interest in machine learning and scientific computing Experience with numerical methods for PDEs and data-driven
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 hours ago
Massachusetts Institute of Technology, Open Learning Position ID: Massachusetts Institute of Technology-Open Learning-MECHE [#31839] Position Title: Position Type: Postdoctoral Position Location
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(SHM), physics-based modeling, and data-driven analytics to enable predictive, performance-based decision-making and improve infrastructure safety, resilience, and lifecycle performance. The candidate is
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will focus on the development of advanced thermoelectric cooler designs for on-chip cooling applications within the MGICIAN Doctoral Network. The project will address model-based and data-driven design
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of Materials, analytical and numerical Data-Driven Engineering Design and Optimization Algorithms Surrogate Modeling (e.g., Kriging, Gaussian Processes, Neural Networks, etc.) Scientific Programming (e.g
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University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture | Croatia | 7 days ago
Engineering and Naval Architecture - Ivana Lucica 5, Zagreb, Croatia indicating „APPLICATION“. Project name: AI-Driven Digital Twin and GIS Optimization for Advanced District Heating Systems Where to apply E
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Energy Environment Water Research Centre (EEWRC) The Climate and Atmosphere Research Centre (CARE-C) The Science and Technology Driven Policy and Innovation Research Centre (STeDI-RC) Considerable cross
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people. By embracing diversity, we believe science can achieve its fullest potential. THE ROLE During your internship you will work on a projectin the Event-Driven Perception for Robotics(https
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want to grow into strong engineers and researchers in either: data & systems for high-frequency pipelines, and/or machine learning models, infrastructure and experimentation Strong fundamentals
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engineering What you will do Develop grey-box models that combine first-principles multiphase flow descriptions with data-driven components Formulate and implement parameter estimation and system identification