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-centric micromanufacturing technologies for next-generation healthcare and MedTech applications. The project offers the possibility to pursue the PhD within the network at different universities and
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Are you fascinated by application-oriented research in mathematics and eager to work at the interface of numerical optimization, optical design, and uncertainty quantification? In this PhD project
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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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of complex thermo-mechanical behaviour (e.g., plasticity, damage, fracture) in engineering materials at different length scales, which emerges from the physics and mechanics of the underlying multi-phase
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of engineered underground hydrogen storage in lined rock caverns (LRCs) excavated in hard crystalline rock. Your tasks are to: - develop coupled thermo-hydro-mechanical numerical models to simulate hydrogen
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systems, or continuous-time and discrete-time LTI systems theory is a plus. Experience with mathematical modeling, optimization, numerical computation, algorithm development, or machine learning. Prior
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., plasticity, damage, fracture) in engineering materials at different length scales, which emerges from the physics and mechanics of the underlying multi-phase microstructure. An integrated numerical
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Challenge: Extreme water events threaten people and infrastructure Change: Understand impact dynamics with experiments and numerical simulations Impact: Optimize the design of resilient