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must have a professionally relevant background in Applied/Numerical Mathematics, Physics, Fluid Mechanics with a solid education in numerical methods for solving partial differential equations. Master
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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
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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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host the PhD position. The topic of the PhD fellowship is at the interface of numerical mathematics, fluid mechanics, and ecology. A successful candidate will be offered a three-year position, which
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fellowship is at the interface of numerical mathematics, fluid mechanics, and ecology. A successful candidate will be offered a three-year position, which could potentially be extended with teaching duties
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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