Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- NIST
- Delft University of Technology (TU Delft)
- Argonne
- ETH Zurich
- National Energy Technology Laboratory (NETL)
- National University of Singapore
- Oak Ridge National Laboratory
- AALTO UNIVERSITY
- CEA
- CNRS
- Chalmers University of Technology
- Free University of Berlin
- North Carolina State University
- Princeton University
- Stony Brook University
- The University of Manchester
- University of North Carolina at Chapel Hill
- University of Texas at Dallas
- Aarhus University
- Basque Center for Applied Mathematics
- FAPESP - São Paulo Research Foundation
- Goethe University Frankfurt
- Harvard University
- ICMAB
- ICN2
- Institute of Fundamental Technological Research Polish Academy of Sciences
- KTH Royal Institute of Technology
- Max Planck Institute for Sustainable Materials •
- Nanyang Technological University
- SUNY University at Buffalo
- Stockholm University
- Technical University of Denmark (DTU)
- University Muenster
- University of Amsterdam (UvA)
- University of Bath
- University of Birmingham
- University of California
- University of Glasgow
- University of Lille
- University of South Carolina
- University of Southern California
- University of Sydney
- Uppsala universitet
- 33 more »
- « less
-
Field
-
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
-
spin-lattice dynamics simulations, a framework that combines atomistic spin dynamics (magnons) with molecular dynamics (phonons), to investigate ways of manipulating spins in various magnetic systems
-
, transfer learning, uncertainty quantification, multitask learning, or learning from sparse and expensive scientific data. Familiarity with atomistic or molecular-simulation software and interfaces, such as
-
Domaine Mathématiques, information scientifique, logiciel Contrat Post-doctorat Intitulé de l'offre Using generative AI to simulate chemically disordered nuclear materials at the atomic level H/F
-
adaptive integration methods designed to accelerate atomistic simulations. The approach to be developed will initially integrate spectroscopy data (XPS, SAX, SXRD) to generate candidate structures for S-S
-
atomistic resolution. However, many chemically relevant processes involve rare events and activated transitions that occur on timescales inaccessible to conventional molecular dynamics simulations. Overcoming
-
and multiscale modelling of helium effects in irradiated Fe–Cr alloys. The position focuses on computational materials science and atomistic simulations. The successful candidate will contribute
-
experience in atomistic simulations is preferred. • Development or working experience of generative models in scientific applications (e.g., diffusion/flow model) and working experience with agents
-
the preponderance of surface and finite size effects. This project aims to dynamically track temperature-induced phase transitions at the nanoscale through atomistic simulations. Focusing on metallic alloys and
-
design rules to understand their chemistry and physics. You will combine coarse-grained and atomistic simulations with surrogate models and experimental insights (with Dr. Baumgartner) to understand