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experience on geotechnical engineering Relevant research experience in molecular dynamics simulations and computational fluid dynamics will be preferred. Excellent proposal writing and report writing skill
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
dynamics (MD) simulations of different materials families composed of Ti and C. Titanium carbides, for example, exhibit exceptional hardness, high melting point, wear and abrasion resistance, and many other
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 1 month ago
complexes. The successful candidate will develop novel graph neural network (GNN) architectures to learn dynamic information from molecular dynamics (MD) simulations of protein-protein and protein-nucleic
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apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular dynamics (MD) simulations
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(Machine Learning Force Fields, ML-FF) to simulate gas-phase fragmentation processes via molecular dynamics. Design and maintain a project-focused results database (schema, metadata, upload/retrieval scripts
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for the simulation of non-adiabatic exciton transfer dynamics in light-harvesting complexes. The research will use a combination of quantum and molecular dynamics simulations, electronic structure calculations, and
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simulations, design and conduct experiments, and analyze multimodal data streams in a continuous, real-time loop with minimal human intervention (https://www.nature.com/articles/s41524-024-01423-2 , https
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modeling and simulation for catalysis and energy applications, and 2) high throughput materials invention and development for extreme environments are of particular interest. Some areas of emphasis include
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prerequisite (i.e., familiarity with linux, bash, conda, python). Experience in molecular dynamics simulation, protein chemistry or phylogenetics would be major assets. An interest in developing wet lab skills
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advances generative models, molecular simulations, and molecular design pipelines to meet pressing challenges in data-driven molecular sciences. The environment is highly collaborative, bringing together