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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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meet the requirements for admission to the Faculty's Doctoral Programme . Experience with optimization modelling and numerical optimization tools, such as JuMP, Pyomo, Gurobi, CPLEX, HiGHS, or similar
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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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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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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