423 machine-learning-"https:"-"https:"-"https:"-"https:"-"RAEGE-Az" positions at Nanyang Technological University
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
, modulation instability, and supercontinuum generation. Integrate experimental data with AI models, using machine learning to uncover hidden physics, accelerate simulations, and discover new operational regimes
-
in empirical analysis using econometric, machine-learning, and language-modeling techniques. Conducting literature reviews and synthesizing existing academic research to support ongoing projects
-
documentation. Applied Machine Learning: Possess deep, practical knowledge of machine learning fundamentals, with proven experience applying algorithms to solve problems in areas like NLP, Computer Vision, or
-
scientific leaders and researchers. Job responsibilities The project aims to advance the use of machine learning techniques to model and understand plasma turbulence in magnetically confined fusion plasmas
-
through to deployment and documentation. Applied Machine Learning: Possess deep, practical knowledge of machine learning fundamentals, with proven experience applying algorithms to solve problems in areas
-
through to deployment and documentation. Applied Machine Learning: Possess deep, practical knowledge of machine learning fundamentals, with proven experience applying algorithms to solve problems in areas
-
aims to improve electrodialysis (ED) for REE separation by developing advanced membranes and integrating AI-driven optimization techniques. By combining materials innovation with machine learning
-
data analysis through to deployment and documentation. Applied Machine Learning: Possess deep, practical knowledge of machine learning fundamentals, with proven experience applying algorithms to solve
-
: Electrochemical process on interface phenomena Battery testing under different conditions Simulation of scaled up process. Interface with machine learning group on data base set up Battery safety testing Presenting
-
in numerical analysis, partial differential equations (PDEs), and scientific computing. Solid background in machine learning theories, with specific experience in Physics-Informed Machine Learning