87 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" uni jobs at Chalmers University of Technology
Sort by
Refine Your Search
-
of Computer and Network Systems , we design secure, dependable and high-performance computer and communication systems that meet the demands of an increasingly digital and interconnected world. About the
-
technology, driven by high-quality research and education, openness and collaboration. As a Teaching Fellow, you will contribute to this goal through engaging teaching and learning in a collegial and inclusive
-
Engineers. The offices also host computer infrastructure and machine learning/data science/research data management experts, who develop, build, and manage the local and national resources for large-scale
-
deployments or data collection in real-world environments) Familiarity with current AI technologies (e.g., machine learning, large language models) and an interest in their application to embodied systems. What
-
data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The applicant is expected to develop and apply data-driven and machine learning-based methods
-
an innovative spirit, in close collaboration with wider society. Chalmers was founded in 1829 and has the same motto today as it did then: Avancez – forward. Where to apply Website https://academicpositions.com
-
the start. The position is a fixed-term appointment of four years, with the possibility to teach up to 20%, which extends the position up to five years. A starting salary of 34,550 SEK per month (valid from
-
conducting research "in the wild" (e.g., field deployments or data collection in real-world environments) Familiarity with current AI technologies (e.g., machine learning, large language models) and an
-
at the division of Computer and Network Systems , where we design secure, dependable and high-performance computer and communication systems that meet the demands of an increasingly digital and interconnected world
-
complex behavior under demanding operating conditions presents a significant modeling challenge. This project addresses that challenge by combining machine learning with constitutive modeling, while