30 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" positions at KTH Royal Institute of Technology in Sweden
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Project description Third-cycle subject: Technology and learning Teacher
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attractive benefits. 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
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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 to work and grow at KTH. Trade union representatives
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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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manufacturing. It is meritorious to have previous experience in data analysis and processing with Python (or similar), preferably including documented experience with machine learning tools. It is meritorious
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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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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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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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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