84 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "FORTH" uni jobs at Chalmers University of Technology
Sort by
Refine Your Search
-
Helps us to derive novel climate data by combining two of Europe's new satellite sensors. If you have interests in physics, climate and machine learning, this is the Doctoral student position
-
combining two of Europe's new satellite sensors. If you have interests in physics, climate and machine learning, this is the Doctoral student position for you! About us Our team is part of the Division
-
at the Division of Data Science and AI at the Department of Computer Science and Engineering . Join our innovative team and contribute to exciting research in theory of machine learning, in a collaborative and
-
-relatedness; derivation of uncertainty bounds; and a rigorous analysis of learning rates. Who we are looking forThe following requirements are mandatory: To qualify as a Doctoral student, you must have a
-
slowdown at the glass transition, remains a major computational challenge. This Doctoral student project addresses this by combining generative AI models and machine-learned interatomic potentials
-
identification and machine learning is a merit. What you will do Perform research, developing your own scientific concepts and communicating the results verbally and in writing Take courses at an advanced level
-
. The main research problems include mathematical theory, algorithms, and machine learning (deep learning) for inverse problems in artificial intelligence, as well as application to medical problems. About the
-
application: Experience in system identification and machine learning is a merit. What you will do Perform research, developing your own scientific concepts and communicating the results of your research
-
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
-
complex behavior under demanding operating conditions presents a significant modeling challenge. This project addresses that challenge by combining machine learning with constitutive modeling, while