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Field
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optimization and applications to large scale problems, nonlinear preconditioners in high performance computing, computational mechanics, the PETSc toolkit, or PDE-constrained optimization. Any project is
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The Faculty of Engineering, Department Applied Physics and Photonics, is looking for a PhD-student with a doctoral grant. More concretely your work package, for the preparation of a doctorate
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PostDoc/Senior Scientist - Process and Plant Design in the Field of Liquid Organic Hydrogen Carriers
languages (e.g., Python, Julia, Matlab) Strong interest in process modeling and simulation, including novel methods and approaches such as neural networks and nonlinear optimization Ability to analyze complex
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and compare/interpret field data Collaborate with mechanical design and manufacturing teams to develop solutions that optimize performance while ensuring manufacturability Summarize key findings
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, there will also be common meetings with the other 14 PhD students in the doctoral network, including 3 training schools. As a participant of the project, the PhD student will become part of a team at DTU with
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samples. Optimize reconstruction algorithms for efficient large-scale 3D imaging, including high-performance and GPU-accelerated computing where appropriate. Design, optimize, and validate a refractive
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aims to develop hybrid quantum–classical approaches for modeling multiphase flows governed by complex, nonlinear dynamics across multiple scales. The postdoctoral researcher will investigate how
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of thick and strongly scattering samples. Optimize reconstruction algorithms for efficient large-scale 3D imaging, including high-performance and GPU-accelerated computing where appropriate. Design, optimize
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improvements. Examples include optimizing the squeezing of the vacuum to minimize quantum noise, a prototype cryogenic interferometer, using machine learning for nonlinear feedback control, devising techniques
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, 100% funded PhD student position to fill starting around June 2026. Research is to be in the field of computational methods in nonlinear and large scale optimization / inverse problems or in novel