18 combinatorial-optimization positions at Chalmers University of Technology in Sweden
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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systems. This PhD project, part of a national initiative, aims to use AI to design and optimize thermal interface materials (TIMs). It combines machine learning, materials informatics, and experiments
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. The postdoctoral project will be focused on the design of optimized generation schemes for tailor-made electron beam and radiation sources in laser-produced plasmas. As a postdoctoral scientist, you are employed by
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of numerical modelling (e.g. CFD, FEA, FSI, optimization, ML), but we are also involved in experiments and real-life monitoring to support our findings. Besides research, our division is actively involved in
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is meritorious for future research duties within academia as well as industry/the public sector. The postdoctoral project will be focused on the design of optimized generation schemes for tailor-made
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, design and characterization of quantum processors Development and optimization of nano-fabrication processes for large-scale devices Development of optimal control techniches to achieve fast and high
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on building the next generation of quantum processors based on superconducting circuits. To achieve this ambitiuous goal, we have a variety of projects related to: Development and optimization of nano
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remediation. This project introduces a bottom-up approach to network security, integrating physical-layer perspectives into the design and optimization of future network services to enhance resilience against