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will consider techniques like flow matching, and use ideas from optimal transport and neural (stochastic) differential equations, invariant Kalman filtering and geometric numerical integration
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, structure preserving deep learning, stochastic differential equations, generative AI, numerical optimization. Strong programming skills (Python, Julia, Jax). Experience with numerical optimization is also
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, NTNU, will host the PhD position. The topic of the PhD fellowship is at the interface of numerical mathematics, generative AI models, and computer science. A successful candidate will be offered a three
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proficiency in at least one language (e.g., Python, Matlab, C++, etc.) Documented competence in at least two of these areas: numerical modelling, artificial intelligence/machine learning, optimization PLEASE
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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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Trondheim. The Department of Mathematical Sciences, NTNU, will host the PhD position. The topic of the PhD fellowship is at the interface of numerical mathematics, generative AI models, and computer
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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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Evan Solbraa (EPT). About the project The position is a part of the AI-Powered integrated Reservoir, flow, and process Optimization (AI-PRO) project. The goal of the project is to produce generalizable
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will feed into an automated stope design workflow, producing variable-length, locally adaptive geometries that optimize the balance between stability, recovery, and dilution control. Through
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systems based on absorption high-temperature heat pumps, with particular emphasis on dynamic behavior and system integration. The research will combine dynamic modelling, numerical simulation, and