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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
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results with linear theory dispersion relations', Phys. Plasmas, vol. 30, no. 1, p. 012104, Jan. 2023, doi: 10.1063/5.0119255. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR7648-ALEALV
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have experience of using molecular dynamics and/or density functional theory methods and a proven ability to structure, manage and work with quantitative data. You will be able to evidence designing and
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and endolysosomal mechanisms; and (3) theoretical and computational neuroscience, including models, algorithms, or theory that probe mechanisms of neural circuit function across levels of biological
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Institute of Neuroscience and physiology, Sahlgrenska Academy, university of Gothenburg | Sweden | 17 days ago
employees make the university a large and inspiring place to work and study. Strong research and attractive study programmes attract researchers and students from around the world. With new knowledge and new
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to density functional theory (DFT) calculations and the generation of high-quality atomistic datasets for the development of analytical bond-order potentials (ADP) and machine-learned interatomic potentials
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language models, reinforcement learning, agent learning, robotic control; (4) quantum materials simulation, density functional theory, catalysis and transition-state theory, molecular dynamics; (5
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electronic structure methods such as density functional theory or many-body techniques, is highly desirable. The candidate should demonstrate strong analytical skills, motivation to work at the interface
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provided by UCD and CERN. This requires advances in theory and simulation in collaboration with Ulster University utilising AI and high-performance computing facilities in Walton Institute SETU, thus
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-driven materials design" https://www.nature.com/articles/s41524-020-00440-1 2. https://jarvis.nist.gov/ 3. https://www.nist.gov/people/kamal-choudhary Machine learning; Density functional theory; force