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Functional Theory (DFT), machine-learned force fields (MLFF), graph neural networks (GNNs), or large language models (LLMs). Extensive Knowledge In: • First-principles atomistic simulations with packages
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' (http://www.quanthic.org ) within the Institut of Chemistry for Life and Health Sciences at Chimie ParisTech -PSL (Paris, France). This work is financed under the ANR ElectroHIP project. Where to apply
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The candidate will be involved in academic research under supervision of Prof. Dr. Roberto Iglesias. - Multiscale modelling using DFT and IA techniques. - Multiscale modelling using Molecular Dinamics and IA
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synthesis. SERS detection. DFT calculation. Where to apply Website https://sede.uvigo.gal/public/catalog-detail/27660208 Requirements Research FieldChemistry » Physical chemistryEducation LevelPhD
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functional theory (DFT), anharmonic lattice dynamics, machine learning force fields, transport, and high-performance computing (HPC). Position Specific Responsibilities/activities: Perform ab-initio
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approaches for constructing 2D heterostructures for DFT calculations Develop, implement, and apply new approaches for constructing 2D heterostructures; compare the results obtained with reference methods
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. Writing scientific articles. Presenting results at scientific conferences. Where to apply Website https://sede.uam.es/sede/piproyectos Requirements Research FieldBiological sciencesEducation LevelPhD
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conceptual DFT (linear response function, Fukui functions) or QTAIM theory (delocalization index), and their validation on a set of compounds known from the literature - interfacing a MLIP (Machine-Learned
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automates building and modifying surface structures, submitting DFT calculations, post-processing electronic structure and vacancy energies, and extracting machine-learning descriptors for modeling oxygen
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learning techniques (RF, XGBoost, NN, SISSO) to screen all the possible compositions of the half-Heusler family in order to find high ZT materials. - Propose new ML methods. - Perform DFT calculations