20 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" positions at KTH Royal Institute of Technology
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scientific curiosity Mastery of data visualization and scientific communication Extensive knowledge of relevant machine learning and AI techniques Self-motivated individual with ability to work independently
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advanced level (higher education) in the research subject or equivalent competence. Experience with deep learning and machine learning tooling.· In-depth knowledge of LLMs and Transformer architectures
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located at SciLifeLab in Stockholm. Our research is focused on cell biology, spatial proteiomics and machine learning for bioimage analysis. The aim is to understand how human proteins are distributed in
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graduate degree or an advanced level (higher education) in the research subject or equivalent competence. Experience with deep learning and machine learning tooling.· In-depth knowledge of deep generative
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for the responsible integration of AI in education. You will work in an interdisciplinary research environment spanning human-computer interaction, intelligent tutoring systems, learning analytics, and education, in
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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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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