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of electronic structure and computational materials discovery. In this role you will be performing first principles-based simulations for the description of chemical and structural dynamics in the context
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nanocrystals and carry out Brownian and molecular dynamics simulations of their self-assembly behavior in solution, on surfaces, or trapped at fluidfluid interfaces. 2) Carry out free energy calculations using
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We are looking for a highly motivated postdoc to join the Cvejic Lab with the commence date of July 15th, 2025 or soon thereafter. Information on the department can be found at: BRIC – University
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(ab initio calculations, Molecular dynamics, Computational modeling) and materials science. - Excellent skills and understanding in physical-chemistry of materials. - Excellent verbal and written
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to address major global challenges through molecular-level insights and cross-disciplinary innovation. We offer a creative and stimulating environment where postdocs will develop skills in organic, inorganic
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thereafter. The project Currently, plant ingredients are often refined to almost molecular purity - and then combined again to create structured foods. This isolation is resource intensive, and the removal
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Hydrogen is expected to play a key role in the energy and fuel mix of future sustainable transport systems. However, due to its small and light molecular structure, hydrogen exhibits significantly
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Job Description Department of Molecular Medicine at the University of Southern Denmark (SDU) is seeking a highly motivated postdoc for a three-year position. The position is available from July 1
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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
to collaborative software development and version control systems (Git) # Experience with electronic structure and molecular dynamics simulation codes (VASP, QuantumEspresso, CP2K, LAMMPS) # Motivation to work
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, molecular dynamics simulations using ab initio and machine-learning potentials, and the development or application of machine-learning tools for feature extraction, property prediction, and inverse molecular