13 machine-learning "https:" "https:" "https:" "https:" "https:" uni jobs at KTH Royal Institute of Technology
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with a strong background in machine learning and LLMs, computer science, and modeling. The candidate will join the project “AI-driven predictive maintenance for buildings: Einar Mattsson (EM) - KTH
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degree in machine learning. The successful candidate will be supervised by professor Aristides Gionis (https://www.kth.se/profile/argioni/ ). The research team focuses on developing novel methods
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from talented and highly motivated candidates to pursue a PhD in machine learning at KTH, Sweden, and NTU, Singapore. This is a fully funded, joint doctoral position that will lead to a joint PhD degree
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support the teaching activities courses at KTH and further develop methodologies and algorithms for the quantum computer simulators. Qualifications Requirements A graduate degree or an advanced level
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Computer Science, primarily within the area of machine learning. This is a temporary position at 50% during six months (the percentage and duration may be adjusted depending on starting date). For information about
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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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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