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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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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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experience in agronomy, soil science, engineering methods, and precision agriculture in semi- and hyper-arid regions. Specifically, we will be testing the effects of soil amendment technologies pioneered by
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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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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
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of bioinformaticians, computer scientists, biotechnologists, biologists, and biochemists. The successful candidate will also enjoy an environment aimed to facilitate progress in the research career: networking, student
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using a combination of multimodal imaging, computer vision, and lab automation platforms that govern entire workflows (e.g. ThermoFisher momentum software scheduling Hamilton liquid handlers and high-end
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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