Postdoctoral Fellow - Atomistic Simulations and AI for Materials Design jobs in United States
cer-icon
Apply on Employer Site
company-logo

Inside Higher Ed · 1 week ago

Postdoctoral Fellow - Atomistic Simulations and AI for Materials Design

Johns Hopkins University is inviting applications for a Postdoctoral Fellow position in the AtomGPTLab, led by Dr. Kamal Choudhary. The role involves developing a computational platform that integrates atomistic simulations and AI techniques to enhance the discovery of novel materials.

Digital MediaEducationHigher EducationJournalismRecruiting

Responsibilities

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 undergraduate and graduate students, and participate in grant proposal development

Qualification

Density Functional Theory (DFT)Machine-learned force fieldsGraph neural networks (GNNs)Large language models (LLMs)First-principles simulationsPythonHPC environmentsData integrationInterdisciplinary collaborationMentoring

Required

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 simulations with packages such as VASP, Quantum ESPRESSO, GPAW
Machine-learned interatomic potentials (e.g., ALIGNN-FF)
Structure-property prediction using GNNs (e.g., ALIGNN)
LLM fine-tuning and prompt engineering (e.g., HuggingFace, OpenAI, AtomGPT)
Working Knowledge Of Workflow tools (e.g., JARVIS-Tools, 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 students or junior researchers

Benefits

Total Rewards
Health, life, career and retirement

Company

Inside Higher Ed

twittertwittertwitter
company-logo
Inside Higher Ed is the online source for news, opinion, and jobs related to higher education.

Funding

Current Stage
Growth Stage
Total Funding
unknown
2022-01-10Acquired
2006-08-31Series Unknown

Leadership Team

leader-logo
Stephanie Shweiki
Director, Foundation Partnerships
linkedin
Company data provided by crunchbase