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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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research partners. Support the supervision of PhD and MSc students.
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manipulation for high-performance SWIR and exploratory room-temperature MWIR detection. Candidates should hold a PhD in chemistry, materials science, electrical engineering, applied physics, or a related field
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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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headed by famous theorist Daniel Loss Qualifications: PhD in Physics (completed within the last 5 years or near completion); strong background in theoretical condensed matter physics; experience with
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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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are especially interested in candidates with a PhD in Materials Science, Chemistry, Physics, Chemical Engineering, or a related field, and with a solid experimental background in organic semiconductors, polymer
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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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-performance SWIR and exploratory room-temperature MWIR detection. Candidates should hold a PhD in chemistry, materials science, electrical engineering, applied physics, or a related field, with expertise in
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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