Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- Delft University of Technology (TU Delft)
- FAPESP - São Paulo Research Foundation
- Hanyang University
- Institute of Fundamental Technological Research Polish Academy of Sciences
- Iowa State University
- Karlstad University
- Monash University
- NIST
- Oak Ridge National Laboratory
- Tufts University
- Universidade do Minho
- University of Central Florida
- University of Kansas
- University of Leeds
- University of South Carolina
- University of Texas at El Paso
- 6 more »
- « less
-
Field
-
compositionally complex circular steels. As a PhD researcher, you will: Perform Density Functional Theory (DFT) calculations to model hydrogen-tramp element co-segregation at grain boundaries and phase boundaries
-
materials, organic solids, molecular materials, or other solid-state systems. Experience with computational approaches relevant to structural characterization, including density functional theory (DFT
-
dynamics, and density functional theory (DFT) can be combined to accelerate the discovery and design of next-generation organic semiconductor materials with tailored optoelectronic properties. The project
-
position within a Research Infrastructure? No Offer Description Activities and context: The fellow will develop machine-learning interatomic potentials (MLPs), trained on density functional theory-DFT data
-
will elucidate how silicon disrupts copper wetting and diffusion. A central aspect of this project is the development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential
-
development of a Density Functional Theory (DFT)-accurate machine-learned interatomic potential (MLIP) for the multi-component steel system of interest. Ultimately, this simulation-driven framework will allow
-
following areas: Computational materials science Computational metallurgy Integrated Computational Materials Engineering (ICME) CALPHAD and computational thermodynamics Phase-field modeling Density Functional
-
have experience of using molecular dynamics and/or density functional theory methods and a proven ability to structure, manage and work with quantitative data. You will be able to evidence designing and
-
theoretical research on magnetic and topological properties in van der Waals materials using Density Functional Theory (DFT) calculations, tight-binding and machine learning methods. Provide theoretical
-
. An ideal candidate should have experience in modeling electrochemical reactions on surfaces and interfaces using first-principles density functional theory (DFT), grand canonical DFT (GC-DFT