Sort by
Refine Your Search
-
Employer
-
Field
-
or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application of Large Language Models Time-Series Data Prediction and Modeling Intelligent Decision-Making
-
, Automation, Artificial Intelligence, Operations Research, or a related field. Solid research background and practical experience in one or more of the following areas: Reinforcement Learning / Deep
-
-- Deep learning for nuclear physics -- Effective field theory for nuclear structure -- Hard Probes of Quark-Gluon Plasma -- Hot and cold lattice QCD -- Physics in electron-ion collisions -- Relativistic
-
Research, or a related field. Solid research background and practical experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application
-
experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application of Large Language Models Time-Series Data Prediction and Modeling Intelligent
-
are expected to have a Ph.D. in theoretical particle physics or related areas prior to the time of employment. Preferences will be given to those with experiences in collider phenomenology, machine learning
-
than 36. The possible areas of expertise of an applicant are pure, applied or computational mathematics. The successful candidate will have no obligation to teach, but will be required to apply
-
, with a strong interest in interdisciplinary research in biophysics. 2. Have a professional background in physics or mathematics or interdisciplinary biophysics, and experience in machine learning is
-
, Jiangsu, China [map ] Subject Areas: Artificial Intelligence / AI/Machine Learning High Energy Physics / BSM , BSM new physics , Electroweak Symmetry Breaking , Higgs physics , Particle Theory
-
on innovative learning and teaching, and research, XJTLU draws on the strengths of its parent universities, and plays a pivotal role in facilitating access to China for UK and other institutional