148 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "FORTH" positions at Chalmers University of Technology in Sweden
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learning-based control within a collaborative and dynamic environment. About us At the department of Electrical Engineering research and education are performed in the areas of Communications, Antennas and
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deployments or data collection in real-world environments) Familiarity with current AI technologies (e.g., machine learning, large language models) and an interest in their application to embodied systems. What
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to teach on the undergraduate/master’s level. The position is meritorious for future roles in academia, industry, or the public sector. Contract terms Full-time temporary employment for a maximum of two (2
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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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complex behavior under demanding operating conditions presents a significant modeling challenge. This project addresses that challenge by combining machine learning with constitutive modeling, while
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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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the start. The position is a fixed-term appointment of four years, with the possibility to teach up to 20%, which extends the position up to five years. A starting salary of 34,550 SEK per month (valid from
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slowdown at the glass transition, remains a major computational challenge. This Doctoral student project addresses this by combining generative AI models and machine-learned interatomic potentials
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and automated floor-plan recognition, to fill data gaps and harmonise information from disparate sources. Learn more and watch our project video here: https://sb.chalmers.se/digital-material-inventories
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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