Postdoc in AI-Driven Atomistic Materials Design

Inside Higher Ed; Baltimore (MD); On-site; USD 70,000 - 90,000; Full time

Benefits offered by this job

Access to cutting-edge computing clusters

Interdisciplinary collaboration opportunities

Participation in international conferences

Job summary

A prestigious research university seeks a Postdoctoral Fellow to lead in atomistic simulations and machine learning applications. Candidates must hold a PhD in related fields and demonstrate expertise in DFT, graph neural networks, and AI techniques. This position offers a collaborative environment, extensive resources, and opportunities for interdisciplinary research. Responsibilities include developing predictive models and managing large-scale datasets in material science, with an engaging role in mentoring students and driving innovative research projects.

Qualifications

• Demonstrated experience in atomistic simulations, machine‑learned force fields, and artificial intelligence.
• Extensive knowledge in first‑principles simulations and machine‑learned interatomic potentials.
• Experience in mentoring junior researchers and collaborating across disciplines.

Responsibilities

• Conduct DFT calculations and manage large materials datasets.
• Develop GNN architectures for predicting materials properties.
• Fine‑tune or pre‑train LLMs for materials structures generation.

Skills

Density Functional Theory (DFT)

Machine‑learned force fields (MLFF)

Graph neural networks (GNNs)

Large language models (LLMs)

Python

Education

PhD in Materials Science, Physics, Chemistry, Chemical Engineering, Computer Science or related field

Tools

VASP

Quantum ESPRESSO

GPAW

HPC environments

JARVIS‑Tools

Job description

A prestigious research university seeks a Postdoctoral Fellow to lead in atomistic simulations and machine learning applications. Candidates must hold a PhD in related fields and demonstrate expertise in DFT, graph neural networks, and AI techniques. This position offers a collaborative environment, extensive resources, and opportunities for interdisciplinary research. Responsibilities include developing predictive models and managing large-scale datasets in material science, with an engaging role in mentoring students and driving innovative research projects.