Postdoctoral Appointee - Building Agentic AI Platform for X-ray Science
Core
Develop an AI-enabled platform for X-ray absorption spectroscopy by integrating LLMs, scientific machine learning, and physics-aware workflows to advance agentic AI tools for simulation, interpretation, and scientific discovery.
Role type
Postdoctoral Researcher (Agentic AI for X-ray Science)
Builds
Agentic AI tools for simulation, interpretation, data analysis, and scientific discovery in X-ray science
Domain
X-ray Science / Computational Chemistry / Scientific Machine Learning
Deliverable
production ML models
Required skills
DFT and electronic-structure theory, atomistic simulations, Python, LLM APIs, agent frameworks, PyTorch, scientific stack (numpy, pandas, scikit-learn), materials science domain knowledge
Preferred skills
X-ray absorption spectroscopy theory (XANES/EXAFS), XAS simulation packages (FEFF, OCEAN, FDMNES), high-throughput spectroscopy workflows, HPC, synchrotron datasets, physics-informed AI
Technologies
VASP, Quantum ESPRESSO, CP2K, ABINIT, GPAW, Gaussian, ORCA, Q-Chem, FastAPI, Flask, PyTorch, numpy, pandas, scikit-learn, FEFF, OCEAN, FDMNES, XSpectra
Responsibilities
Integrate LLMs and scientific ML into X-ray absorption spectroscopy workflows; develop agentic AI tools for simulation and data analysis; compare simulated and experimental XAS/XAFS spectra; design and deploy web-based applications and back-end services; manage reproducible analysis or simulation workflows
Seniority
Postdoctoral, hands-on IC