127 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at Chalmers University of Technology
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equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our
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. Chalmers was founded in 1829 and has the same motto today as it did then: Avancez – forward. Where to apply Website https://academicpositions.com/ad/chalmers-university-of-technology/2026/postdoc
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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
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data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The applicant is expected to develop and apply data-driven and machine learning-based methods
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advantage Are curious and motivated to learn Contract terms Temporary employment with a start date as soon as possible, ending end of August. What we offer Chalmers provides a cultivating and inspiring
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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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, such as pulse design or numerical optimization Background in data-driven or machine-learning approaches relevant to optimal control (e.g., model learning, reinforcement learning) What you will do Take
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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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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