Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Princeton University
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- ICN2
- Northeastern University
- CNRS
- KINGS COLLEGE LONDON
- King's College London
- Oak Ridge National Laboratory
- University of California Berkeley
- Aarhus University
- Argonne
- CIC energiGUNE
- CNRS-ENS-UCBL
- Eindhoven University of Technology (TU/e)
- Fundación IMDEA Nanociencia
- Iowa State University
- Massachusetts Institute of Technology
- Nature Careers
- Pennsylvania State University
- U.S. Department of Energy (DOE)
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); Amsterdam
- University of California, Berkeley
- University of Jyväskylä
- University of Kansas
- University of New South Wales
- University of Nottingham
- University of Oregon
- University of Oxford
- University of South Carolina
- 20 more »
- « less
-
Field
-
related field are particularly encouraged to apply.We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular
-
the development of hierarchical computational materials discovery schemes combining random structure searching, machine learning, atomistic, and density functional theory (DFT) calculations to accurately and
-
using molecular mechanics and hybrid QM/MM methods. Applying density functional theory, correlated wavefunction methods (e.g., MP2, CCSD(T)), and multiconfigurational approaches (e.g., CASSCF, CASPT2
-
behavior within the storage system to optimize design and performance. Demonstrated proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics (MD) simulations, enabling the computational
-
advanced characterization methods of inorganic materials and their assemblies, ideally with a focus on battery materials. Demonstrated proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics
-
assemblies, ideally with a focus on battery materials. Demonstrated proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics (MD) simulations, enabling the computational investigation
-
proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics (MD) simulations, enabling the computational investigation of material properties, electronic structure, and atomic-scale behavior
-
to) SIESTA (www.siesta-project.org) and its TranSIESTA functionality. SIESTA is a multi-purpose first-principles method and program, based on Density Functional Theory, which can be used to describe the atomic
-
. Demonstrated experience in performing simulations at the atomic-scale, such as density functional theory (e.g. VASP, Ab-init, Quantum espresso), molecular dynamics (e.g. LAMMPS, DL-POLY), development of inter
-
deposition (ALD). The project involves performing quantum mechanical calculations (e.g., first principles density functional theory (DFT)) to identify the structures and to understand the complex mechanisms