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are important in our work: geometrical optics, ray tracing, (numerical) PDEs, transport theory, nonlinear optimization, Lie operators and Hamiltonian systems. PhD vacancy As part of the research program Optical
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for energy saving purpose. The role will focus on geometric representation, multi-physics numerical simulations, machine learning and manufacturability constrained design optimization given metal additive
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at the frontline of the rapid developing field of spatial omics. Opportunities for professional development and career advancement. A collaborative work environment with numerous partners. Access to state-of-the-art
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chair (F Schwenninger) within the cluster “Mathematical Analysis, Geometry, Numerics and Systems” (MAGNUS) of the Department of Applied Mathematics. The project “Optimal Constants as Analytic Benchmarks
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structures, access to space, multidisciplinary design and concurrent engineering, uncertainty treatment and optimisation, machine learning. (https://www.strath.ac.uk/ ) Task description for your Individual
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of spaceflight experiments and the optimization of spaceflight missions and trajectories. (https://www.irs.uni-stuttgart.de/en/ , www.hefdig.com ) Task description for your Individual Research Project (IRP
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, because the patient becomes a source of ionizing radiation during surgery, this technique raises important radioprotection concerns that must be addressed in optimizing the clinical protocol. Monte Carlo
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-hardware Your Profile: Master and subsequent PhD degree in (astro-)physics, computer science, engineering, or other related fields, with a strong focus on numerical simulations Experience in the development
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dimensionality reduction * development of optimization algorithms and numerical modeling procedures * development of simulation models using commercial packages (CST, ADS, HFSS, etc.) * writing technical reports
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prediction Integration of domain decomposition methods into the learning framework to enable efficient model parallel training Implementation and optimization of GPU-accelerated training pipelines Validation