Postdoctoral Appointee - Building Agentic AI Platform for X-ray Science

We are seeking a highly motivated and creative Postdoctoral Researcher to join the X-ray Science Division (XSD) at Argonne National Laboratory. The successful candidate will develop an AI-enabled platform for X-ray absorption spectroscopy by integrating LLMs, scientific machine learning, physics-aware workflows, and strong computational chemistry/electronic-structure expertise. The researcher will work with a multidisciplinary team to advance agentic AI tools for simulation, interpretation, data analysis, and scientific discovery. The appointment is expected to last two years and the contract is extended yearly.

Position Requirements

• A recent PhD (within 5 years) in computational chemistry, chemistry, materials science, physics, computational science, computer science, engineering, or a related field.

• Strong computational chemistry background in atomistic simulations, electronic-structure theory, DFT, structure-property relationships, and interpretation of simulation results.

• Hands-on experience with DFT or electronic-structure codes such as VASP, Quantum ESPRESSO, CP2K, ABINIT, GPAW, Gaussian, ORCA, Q-Chem, or related packages.

• Strong materials science or chemistry domain knowledge, such as bonding, defects, catalysis, batteries, solid-state chemistry, molecular systems, or related materials classes.

• Strong Python skills and familiarity with LLM APIs, agent frameworks , PyTorch, and the Python scientific stack (e.g., numpy, pandas, scikit-learn). Passion for front-end development and web-based applications, back-end services and API design (e.g., FastAPI, Flask), and deploying applications in local or cloud environments.

• Experience with complex scientific datasets and reproducible analysis or simulation workflows.

• Effective written and oral communications skills.

• Demonstrated ability to work both independently and collaboratively in a multidisciplinary environment.

• Commitment to Argonne's Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

Preferred Knowledge, Skills, and Experience

• Experience with X-ray absorption spectroscopy theory, modelling, and interpretation, including XANES/EXAFS.

• Hands-on experience with XAS simulation packages such as FEFF, OCEAN, FDMNES, XSpectra, or exciting.

• Experience comparing simulated and experimental XAS/XAFS spectra.

• Experience with high-throughput spectroscopy workflows, HPC, synchrotron datasets, or physics-informed AI.

**Please include a cover letter that briefly describes relevant simulation, chemistry, AI/ML, and XAS experience; include code links if available.**

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Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.