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Field
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traffic demands increase, there is a growing need for innovative methods to continuously assess track condition and predict deterioration. This PhD project addresses this challenge by developing a novel
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to understand the conditions and the mechanisms that enable life to emerge. Developing reliable methods to detect reliable traces of life (biosignatures) – whether through remote sensing or direct on-site
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is the development of hybrid models that combine our integrated national model of Denmark with AI-based methods to improve predictions of floods and droughts. We seek to enhance our ability to predict
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, energy systems, computational engineering, or a related field Strong background in numerical methods, mathematical modeling, and network simulation or analysis Good understanding of power systems, gas
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-order modelling, surrogate models, and machine-learning methods such as neural networks. Control design for flexible reactor operation. Develop advanced control strategies that enable safe and efficient
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curious, careful, and motivated by experimental research. You enjoy working with complex geochemical systems and are excited by the challenge of developing new methods and datasets. An MSc degree in
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-tuning, or related optimization methods. Experience with cloud infrastructure, MLOps or LLMOps, containerization, or production deployment. Frontend and/or backend development skills. We offer ETH Zurich
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or more of the following topics: numerical methods for (partial) differential equations. optimization or inverse problems. data assimilation or uncertainty quantification. agentic and generative AI. solid
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qualification in a relevant discipline Good scientific writing Knowledge of advanced statistical methods. Good numerical and statistics skills and familiarity with text editing software, such as Word, Excel, etc
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. Required selection criteria You must have a professionally relevant background in Applied/Numerical Mathematics, Physics, Fluid Mechanics with a solid education in numerical methods for solving partial