Sort by
Refine Your Search
-
of this WASP-financed project is machine learning, in particular dealing with generative models and instabilities associated with cycles of retraining on mixtures of human and machine-generated data
-
met no later than at the time the employment decision is finalized, which occurs when the employment contract is signed. Additionally, you should have a willingness to learn new things and explore
-
approach that integrates wireless communication, computer vision, and machine learning to optimize PC transmission from sensors to an edge server for remote registration. The research is funded by Wallenberg
-
machine learning techniques into a modern AI planning system. The project will involve both theoretical and experimental work As a PhD student, you devote most of your time to doctoral studies and the
-
. The degree requirement must be met no later than at the time the employment decision is finalized, which occurs when the employment contract is signed. Furthermore, you shall be eager to learn new things and
-
, dynamic programming , statistical signal processing, reinforcement learning, and have good programming skills in Python and MATLAB. - Ability to work independently and ability to formulate and tackle
-
, implementation of methods in computer codes, use of state-of-the-art high-performance computers in Sweden and in Europe, application of machine-learning and AI techniques, and collaborations with experimental
-
information and advice to other members of the group on scientific protocols and experimental techniques Take initiatives on research-related tasks Acquire new knowledge Be fluent in both written and spoken
-
techniques Take initiatives on research-related tasks Acquire new knowledge Have prior experimental experience of what is described in the job description above— especially that related to physics, and/or
-
responsibility for own task. Structures own ways of tackling problems and pushes own processes through. Willing to learn new techniques or concepts and apply them in an interdisciplinary research environment