48 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" positions at KTH Royal Institute of Technology
Sort by
Refine Your Search
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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