Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- NIST
- Chalmers University of Technology
- Delft University of Technology (TU Delft)
- Nanyang Technological University
- National University of Singapore
- AALTO UNIVERSITY
- Aarhus University
- Argonne
- CEA
- ETH Zurich
- FAPESP - São Paulo Research Foundation
- Hanyang University
- Institute of Fundamental Technological Research Polish Academy of Sciences
- Iowa State University
- Karlstad University
- Monash University
- Oak Ridge National Laboratory
- The University of Queensland
- Tufts University
- Universidade do Minho
- University of Central Florida
- University of Kansas
- University of Leeds
- University of Nottingham
- University of Nottingham;
- University of South Carolina
- University of Texas at El Paso
- 17 more »
- « less
-
Field
-
-functional-theory (DFT) reference calculations; sample lithiation and phase transitions using Grand-Canonical Monte Carlo coupled to molecular dynamics, including biased sampling when needed; reconstruct
-
of spectroscopic measurements. Investigate underlying receptor-analyte interactions with the aid of in silico calculations, including density functional theory (DFT) simulations. Apply machine learning approaches
-
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
-
successful candidate will conduct Density Functional Theory (DFT) simulations and develop machine learning potentials to investigate zeolite-related systems, particularly under complex chemical environments
-
waste-heat recovery systems; Undertake density functional theory calculations and finite element analysis to guide the selection and development of thermoelectric materials and devices, while contributing
-
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
-
of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
-
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