131 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Ulster University" positions at Chalmers University of Technology
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an innovative spirit, in close collaboration with wider society. Chalmers was founded in 1829 and has the same motto today as it did then: Avancez – forward. Where to apply Website https://academicpositions.com
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-energy devices. Using state-of-the-art electronic-structure calculations and machine learning methods, you will model these effects and contribute to the design of improved semiconductors for solar cells
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-order modeling, or machine learning Experience collaborating in interdisciplinary research teams What you will do Develop hybrid quantum–classical methods to improve simulation and prediction
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computational methodologies, ranging from atomistic and electronic-structure–based materials modeling and characterization, via machine-learning and high-throughput methods, to ab initio calculation
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the last three years prior to the application deadline. Experience in some of the following areas is meritorious: AI and machine learning; convex analysis; functional analysis; mathematical statistics
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, or quantum-inspired methods Experience with hybrid quantum–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience
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dynamics for shape change. A further aspect of the project is learning and calibrating these models from data using data-driven inference methods. Who we are looking for Required qualifications A doctoral
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of advancing Swedish academia and industry to the forefront of quantum technology, and to build a Swedish quantum computer. The student contributes to this project to explore fundamental and applied questions in
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optical microcavities, and similar lines, summarized in these publications: https://www.pnas.org/doi/abs/10.1073/pnas.2505144122 https://www.science.org/doi/full/10.1126/sciadv.adn1825 https
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(e.g., from the viewpoint of physics, chemistry, or mechanical engineering), programming, machine learning, or equipment automation (including microfluidic systems, robotics and remote sensing