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computational engineering, mathematics, computer science, physics, engineering or a related field Strong background in numerical methods and machine learning Proficiency in at least one programming language
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related field Strong background in numerical methods and machine learning Proficiency in at least one programming language (Python, Julia, C++, …) Good analytical skills Good organizational skills and
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results and to make parameter estimation more efficient. The project will apply and evaluate these new methods at different sites and time periods, compare them with established approaches, and finally
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Tensorflow or Pytorch is advantageous Experience in numerical methods for partial differential equations is beneficial Effective communication skills and an interest in contributing to a highly international
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: Investigation of precipitation and crystallization processes in phosphorus recovery using experimental and numerical methods Supervisor: Prof. Sergiy Antonyuk C2: Experimental investigation and modeling
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and ability to work both independently and collaboratively Experience with deep learning frameworks, such as Tensorflow or Pytorch is advantageous Experience in numerical methods for partial
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these new methods at different sites and time periods, compare them with established approaches, and finally demonstrate their potential in a Europe-wide ecosystem reanalysis. The outcomes will include open
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method implemented into the EURAD-IM. In atmospheric chemistry modeling, the 4D-var method is a powerful tool to assess the state of the atmosphere and the corresponding emissions that are in compliance