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streams, contributing to elevated environmental mercury levels and increased human exposure. It is estimated that around 300 tonnes of mercury are released annually through these processes, making them one
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will be as a researcher in a two-year project carried out in close collaboration with our industry partner. The goal is to develop methods for an ML-based decision support system for monitoring and fault
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-year project carried out in close collaboration with our industry partner. The goal is to develop methods for an ML-based decision support system for monitoring and fault diagnosis of gas turbines
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increased human exposure. It is estimated that around 300 tonnes of mercury are released annually through these processes, making them one of the top three sources of anthropogenic mercury emissions worldwide
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criteria: Documented knowledge, preferably from his / her university education, is required in: mathematics, especially differential equations; numerical methods and computer programming; physical
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agents, prompting methods, fine-tuning strategies, and multi-agent systems, and to assess how such methods can be incorporated into the project’s empirical and theoretical work. The position is therefore
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communication networks using AI-powered methods. We will advance the research front in defending future generation networks by: prevention of cyberthreats through anticipating and mitigating them, accurate
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carbon stock estimates within the project “A better check on soil carbon - a novel sampling and measurement approach for improved precision in soil carbon monitoring”. Important parts of the work
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-form methods, and writing or co-authoring manuscripts for high-quality scientific journals. The duties include working both independently and in collaboration with the responsible project manager and
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of advanced modelling and machine learning methods, and may involve the following areas: Dimensionality reduction. Data-driven methods for estimating dynamical models Data-driven methods for estimating