진학프로석·박사 채용 접수 플랫폼

Skills/Qualifications

• • A PhD in Materials Science, Physics, Chemistry, Chemical Engineering, Computer Science, or a related field.
• • Demonstrated experience in one or more of the following: Density 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 such as VASP, Quantum ESPRESSO, LAMMPS, GROMACS.
• • Machine-learned interatomic potentials.
• • Structure-property prediction using GNNs.
• • LLM fine-tuning and prompt engineering (e.g., HuggingFace, OpenAI, AtomGPT).

Working Knowledge Of:

• • Workflow tools (e.g., ASE) and HPC environments.
• • Software development in Python, Git-based version control, and Conda packaging.
• • Data integration and surrogate modeling using experimental and computational datasets.
• • Interdisciplinary collaboration and mentoring of MSc students and PhD researchers

Specific Requirements

• • Conduct high-throughput DFT calculations and manage large-scale materials datasets.
• • Develop GNN architectures for predicting materials properties from atomic graphs.
• • Train and deploy machine-learned force fields for MD simulations and rapid screening.
• • Fine-tune or pre-train LLMs for generation and analysis of materials structures, synthesis protocols, and characterization outputs.
• • Build pipelines for combining experimental and simulated data for inverse design.
• • Provide real-time computational feedback to experimental collaborators for synthesis and characterization.
• • Lead manuscript writing, conference presentations, and contributions to open-source repositories.
• • Mentor Masters and PhD students, and participate in grant proposal development

Additional Opportunities

• • Collaborate on interdisciplinary proposals.
• • Engage with experimental groups, and industry partners.
• • Attend international conferences and contribute to global research communities.
• • Access to cutting-edge computing clusters and experimental characterization tools