Sort by
Refine Your Search
-
Country
-
Employer
- IPCM, CSOB group
- NTNU - Norwegian University of Science and Technology
- University of Groningen
- ; Swansea University
- ; The University of Manchester
- ; University of Birmingham
- ; University of Warwick
- CNRS
- Chalmers University of Technology
- Comenius University Bratislava, Faculty of Mathematics, Physics and Informatics
- ETH Zurich
- Linköping University
- Nantes Université
- Nature Careers
- RMIT University
- Swansea University
- Technical University of Denmark
- University of Iceland
- 8 more »
- « less
-
Field
-
will be put on the effect of structural defects on the electronic properties of the investigated heterojunctions. While we will mainly use density functional theory (DFT) to achieve these goals, we will
-
tuneable materials for Earth-abundant solid-state electrolytes using atomistic simulations (primarily density functional theory, DFT, and molecular dynamics, MD) as well as developing machine learning models
-
Carlo (MC), as well as quantum approaches like Density Functional Theory (DFT). Develop novel Graph Neural Network (GNN) potentials to accurately represent the catalytic behavior of specific species
-
sensing, to improve worker/operator safety. This project will focus on using density functional theory calculations and ab initio molecular dynamics simulations. The project is a collaboration with Dr
-
chemistry software (e.g., Gaussian, Dalton, TurboMole) is highly desirable. Background in Density Functional Theory (DFT) and Time-Dependent Density Functional Theory (TD-DFT) are desirable assets. Strong
-
for the new green steels compositions, including impurities and tramp elements. These models should enable density-functional-theory (DFT) accurate large scale atomistic simulations of defects including
-
density-functional-theory (DFT) accurate large scale atomistic simulations of defects including dislocations, grain boundaries and precipitates, as well as phase diagrams exploration. A key challenge faced
-
oxides, hydroxides and hydrides using a combination of solid-state density-functional theory (DFT) and machine-learning force fields (MLFFs). DFT methods will be used to study materials of interest
-
liquid environment. 1) Molecular Modeling - Study of interactions between MnO₂ and ionic liquids using Density Functional Theory (DFT) and Reactive Molecular Dynamics (ReaxFF). - Analysis of oxidation
-
on the electronic properties of the investigated heterojunctions. While we will mainly use density functional theory (DFT) to achieve these goals, we will also exploit machine-learning techniques to train more