Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- CNRS
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Argonne
- ICN2
- KINGS COLLEGE LONDON
- King's College London
- NEW YORK UNIVERSITY ABU DHABI
- Princeton University
- Uppsala universitet
- AALTO UNIVERSITY
- Duke University
- Eindhoven University of Technology (TU/e)
- Itä-Suomen yliopisto
- Jerzy Haber Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences
- KTH Royal Institute of Technology
- MASARYK UNIVERSITY
- Oak Ridge National Laboratory
- SciLifeLab
- Umeå University
- University of North Carolina at Chapel Hill
- University of Oxford
- University of Southern Denmark
- Utrecht University
- Chalmers University of Technology
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); yesterday published
- Durham University
- Forschungszentrum Jülich
- Fudan University
- Ghent University
- Grenoble INP - Institute of Engineering
- Heriot Watt University
- Inria, the French national research institute for the digital sciences
- Institute of Physical Chemistry, Polish Academy of Sciences
- Japan Agency for Marine-Earth Science and Technology
- Karlstads universitet
- Marquette University
- National Aeronautics and Space Administration (NASA)
- National Centre for Nuclear Research
- Nature Careers
- Northeastern University
- Technical University of Denmark
- Umeå universitet stipendiemodul
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); Published yesterday
- University of California, Merced
- University of Copenhagen
- University of Kansas
- University of New Hampshire
- University of New Hampshire – Main Campus
- University of New South Wales
- University of Texas at Tyler
- University of Utah
- Université de Strasbourg
- Zintellect
- chalmers tekniska högskola
- Łukasiewicz Research Network - Krakow Institute of Technology
- 47 more »
- « less
-
Field
-
. In this role, you will help build a systems-level understanding of human biology by developing computational approaches to model the dynamic regulatory programs of cells and tissues. By combining
-
to its state-of-the-art infrastructure. With an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model. About Entity
-
SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
Computational Chemistry, Materials Science, or a related field. Strong background in computational chemistry techniques, including molecular dynamics, quantum mechanical simulations, and machine learning
-
the next generation of PV technologies for beyond 2030. The new postdoctoral research position will use materials modelling techniques (DFT, molecular dynamics, machine learning potentials) to investigate
-
into cellulose-solvent interactions and fiber formation. The candidate will develop models at different levels, including molecular dynamics, DFT, and mesoscale simulations. This multiscale approach will assist in
-
University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | 2 months ago
USNH Employees should apply within Workday through the Jobs Hub app The research position is within the broad area of biophysics, structural modeling, and molecular dynamics (MD) simulations
-
contribute to various aspects of the project, such as: - developing new theoretical approaches to model electrode/electrolyte interfaces - performing molecular simulations, such as molecular dynamics
-
. You will develop numerical models for nanoscale heat dissipation to interpret the experimental data. The project will be supervised by Prof. Zijlstra (Molecular Plasmonics group) and co-supervised by
-
performing DFT/MLIP molecular dynamics simulations; - Analyzing and exploiting results, as well as writing activity reports and scientific publications and presenting results at conferences and working groups
-
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