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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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Applicants must have a PhD in Computer Engineering, Computer Science, or Electrical and Computer Engineering, and have published their research in prestigious conferences and journals in related
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on the development of new methods integrating a variety of data types (remote sensing, geology, geophysics, geochemistry) for geological modelling and advanced exploration targeting of mineral deposits
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integration methods for the different data types. In terms of applications, the candidate will be free to choose their own case study(s). Additionally, close collaboration with other group members is expected
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the last 5 years or near completion); strong background in theoretical condensed matter physics; experience with analytical many-body methods, field theory, and/or numerical techniques is a plus. Details
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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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technologies. Key Responsibilities: Develop and optimize hard carbon synthesis processes using bio-based and non-bio-based precursors. Explore innovative methods to enhance material properties for energy storage
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family of printable high-gauge factor wireless sensors by building and extending our related background intellectual property. Responsibilities : Collaborate with the development of a new family of RFID
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-based precursors. · Explore innovative methods to enhance material properties for energy storage applications and other emerging technologies. · Conduct detailed structural, chemical, and