Sort by
Refine Your Search
-
Category
-
Country
-
Program
-
Employer
- NIST
- Delft University of Technology (TU Delft)
- National University of Singapore
- Nanyang Technological University
- Argonne
- CNRS
- ICN2
- University of Texas at Austin
- AALTO UNIVERSITY
- Aarhus University
- Autonomous University of Madrid (Universidad Autónoma de Madrid)
- Baylor University
- CEA
- Chalmers University of Technology
- ELETTRA - SINCROTRONE TRIESTE S.C.P.A.
- ETH Zurich
- FAPESP - São Paulo Research Foundation
- Free University of Berlin
- Grenoble INP - Institute of Engineering
- Hanyang University
- ICMAB
- Institut Català de Nanociència i Nanotecnologia
- Institute of Fundamental Technological Research Polish Academy of Sciences
- Iowa State University
- KTH Royal Institute of Technology
- Karlstad University
- Monash University
- NTNU Norwegian University of Science and Technology
- Oak Ridge National Laboratory
- Tufts University
- Universidade do Minho
- University of California
- University of California, Merced
- University of Central Florida
- University of Girona (UdG) - Institute of Computational Chemistry and Catalysis (IQCC)
- University of Leeds
- University of South Carolina
- University of Southern California
- University of Sydney
- University of Texas at El Paso
- University of Warwick;
- 31 more »
- « less
-
Field
-
(DFT) simulations and develop machine learning potentials to investigate zeolite-related systems. The role will focus on delivering research projects and promoting research excellence in this area. The
-
Responsibilities Theory Quantum transport modeling using NEGF; first-principles materials and interface calculations using DFT (VASP, Quantum ESPRESSO, or equivalent). Atomistic spin dynamics
-
molecular dynamics simulations to model structural, thermodynamic, and electronic properties. - Contribute to open-source software, benchmarks, and datasets that advance the global materials community
-
-throughput first-principles (DFT/MD) simulations, and generative AI to predict, interpret, and design materials for energy storage, energy conversion, and electronic applications. The successful candidate will
-
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
-
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
-
on developing AI-driven methods for catalyst and materials discovery, atomistic simulations, and data-driven understanding of complex energy systems. Key Responsibilities: Research Outputs: Expected to lead 1–2
-
Theory (DFT) Microstructure prediction and evolution Secondary expertise in the following topics is desired but not required: Molecular dynamics Materials informatics Machine learning and artificial
-
predictive MD simulations capable of resolving atomic-scale LCI mechanisms with near-DFT accuracy Investigate how silicon suppresses LCI, including its effects on grain boundary site competition and the
-
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