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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
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The School of Mechanical & Aerospace Engineering (MAE) is a robust, dynamic and multi-disciplinary international research community comprising of world-class scientists and bright students. MAE
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HBIGS Heidelberg Biosciences International Graduate School | Heidelberg, Baden W rttemberg | Germany | 4 days ago
to investigate Wnt signaling in different model systems, including Drosophila, Hydra and cultured cells. Technology development will be in the areas of precise and efficient gene tagging, pooled in vitro and in
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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-PHYSICS [#31836] Position Title: Position Type: Postdoctoral Position Location
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platform strategy, network effects, or digital business models Experience with vertical integration strategies in technology or platform businesses Track record in repositioning companies from marketplace to
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of the Universitat Politècnica de València announcing a selection process for an indefinite employment contract within the framework of a Line of Research: R&D line: Data-driven network modeling and optimization https
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support research activities in power system resilience optimization and asset health modelling. The role focuses on developing data-driven and physics-informed analytical models to enhance reliability, risk
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of Systems and Control, Department of Electrical Engineering, focused on innovative research in Data-driven Modelling and Optimisation for Fast Charging of Lithium-Ion Batteries. About us At the department