Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
. Techniques/methods in use: Density Functional Theory, Surface X-ray Diffraction, Surface Science Applicant skills: Strong background in chemistry, physical chemistry, materials science, or condensed matter
-
the consideration of transfer learning, machine learning interaction potentials, and existing knowledge from experimental studies. Techniques/methods in use: Density Functional Theory, Machine Learning Applicant
-
, HEP-Theory (hep-th) , High energy density matter , High Energy Experimental , High Energy Physics , high energy physics or mathematical physics , High Energy Theory , High Energy Theory Group , High
-
Laboratoire de Chimie Théorique, Sorbonne Université & CNRS | Paris 15, le de France | France | 11 days ago
not yet exist. The project aims to develop new electronic structure methods, in particular within the framework of density functional theory (DFT), based on a variational formulation of QED. The work
-
where theoretical understanding remains limited. The project combines next-to-leading order calculations within the Colour Glass Condensate (CGC) effective theory with phenomenological studies of key
-
programming. Deep knowledge of electronic structure theory and a strong foundational understanding of solid-state physics. Experience with Density Functional Theory (DFT) and a high interest in an academic
-
knowledge of electronic structure theory and a strong foundational understanding of solid-state physics. Experience with Density Functional Theory (DFT) and a high interest in an academic career. Strong
-
-driven materials design" https://www.nature.com/articles/s41524-020-00440-1 2. https://jarvis.nist.gov/ 3. https://www.nist.gov/people/kamal-choudhary Machine learning; Density functional theory; force
-
energy density matter , High Energy Experimental , High Energy Physics , high energy physics or mathematical physics , High Energy Theory , High Energy Theory Group , High Performance Computing
-
performance, yet their atomic-scale origin and role in reactivity remain poorly understood. The project addresses this open problem by integrating high-throughput Density Functional Theory, machine-learning