48 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions at KTH Royal Institute of Technology
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applications for promotion to associate professor, provisions according to Section 1.2.4 of Appointments procedure at KTH will be applied. Ability to teach in Swedish is a merit that is given great importance in
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lifetime. Most studies on this topic explore optimization and deep learning methods for finding the global optimal solutions offline and verify the results by model-in-the-loop simulations, but this project
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. Equality, diversity and equal opportunities are essential to quality and form an integral part of KTH’s core values as a university and public authority. Learn more about our benefits and what it's like
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an integral part of KTH’s core values as a university and public authority. Learn more about our benefits and what it's like to work and grow at KTH. Trade union representatives Contact information to trade
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that include machine learning components, and on cooperation with industrial partners and with the TECoSA competence center at KTH. The Division of Network and Systems Engineering conducts fundamental research
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KTH’s core values as a university and public authority. Learn more about our benefits and what it's like to work and grow at KTH . Trade union representatives Contact information to trade union
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reconstruction. We will use physics modeling, machine learning and experiments to develop new and improved methods for using data from energy-sensitive x-ray detectors to improve the diagnostic quality of x-ray
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5 Dec 2025 Job Information Organisation/Company KTH Royal Institute of Technology Research Field Computer science » Computer architecture Computer science » Programming Computer science » Other
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an interest in Bayesian statistics, applied probability theory, computational mathematics, machine learning, and generative AI, and offers the opportunity to contribute to a rapidly growing research field with
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experimental platform and combine it with continuum modeling of complex materials and machine-learning-based analysis methods to understand and predict biofilm structure and growth. Supervision: Shervin Bagheri