Postdoctoral Appointee – Materials Informatics and Autonomous Synthesis

Argonne National Laboratory; Lemont (IL); On-site; USD 72,879 - 121,465; Full time

Benefits offered by this job

Comprehensive benefits

Collaborative work environment

Job summary

The Center for Nanoscale Materials at Argonne National Laboratory seeks a postdoctoral researcher to develop AI/ML methods for autonomous materials discovery and synthesis. Ideal candidates will have a PhD in a relevant field and experience in machine learning and scientific computing.

This full-time position offers a competitive salary range of $72,879 to $121,465, alongside comprehensive benefits. Applicants must be passionate about integrating data science with experimental workflows and contributing to collaborative research efforts.

Qualifications

• Recent or soon-to-be-completed PhD in a relevant field.
• Demonstrated accomplishment in materials informatics or scientific machine learning.
• Strong skills in Python and scientific computing.

Responsibilities

• Develop machine-learning-ready data resources for materials.
• Build surrogate and predictive models.
• Design active learning and Bayesian optimization workflows.

Skills

Python

Machine Learning

Data Science

Scientific Computing

Education

PhD in chemistry, chemical engineering, materials science, polymer science, physics, computer science, or data science

Tools

NumPy

pandas

scikit-learn

PyTorch

TensorFlow

Job description

The Center for Nanoscale Materials (CNM) at Argonne National Laboratory invites applications for a postdoctoral research position focused on developing AI/ML methods for autonomous materials discovery and synthesis. The role is ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experimental science, and who is motivated to translate computational advances into real laboratory workflows.

Key Responsibilities

• Develop machine‑learning‑ready data resources for materials by integrating literature, in‑house, and newly generated experimental data.
• Build surrogate and predictive models that connect composition, molecular structure, synthesis and processing conditions, morphology, and device‑relevant properties.
• Design active learning, Bayesian optimization, uncertainty‑aware modeling, and other adaptive experimental design workflows to guide experiments and improve data efficiency in autonomous platforms such as the Polybot.
• Work closely with experimental researchers to integrate AI/ML workflows into closed‑loop autonomous synthesis, fabrication, and characterization; translate model predictions into experimental campaigns; and update models using newly acquired data.
• Contribute to strategies for generating diverse, high‑value datasets, identifying meaningful descriptors and representations, and building reproducible computational pipelines, workflow automation, and data infrastructure that support long‑term autonomous laboratory capabilities.
• Share research outcomes through publications, presentations, software, datasets, and internal reports.

Position Requirements

• Recent or soon‑to‑be‑completed PhD (within the last 0–5 years) in chemistry, chemical engineering, materials science, polymer science, physics, computer science, and/or data science.
• Demonstrated accomplishments in materials informatics, scientific machine learning, or AI‑guided experimental design.
• Strong Python and scientific computing skills, including experience with NumPy, pandas, scikit‑learn, and machine‑learning frameworks such as PyTorch or TensorFlow.
• Experience developing surrogate models, predictive models, or adaptive learning workflows for scientific or engineering applications.
• Strong interest in working closely with experimental researchers in a laboratory‑centered environment.
• Evidence of independent research productivity through publications, software, datasets, or similar outputs.
• Excellent communication skills and the ability to work effectively in interdisciplinary teams.
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

Preferred Qualifications

• Experience with active learning, Bayesian optimization, adaptive experimental design, reinforcement learning for experiments, or uncertainty quantification.
• Experience with autonomous, self‑driving, or robotic laboratory platforms.
• Background in electronic polymers, conjugated polymers, organic semiconductors, soft materials, electrochemical materials, or related functional materials.
• Experience integrating literature, experimental, and simulation datasets into unified, machine‑learning‑ready workflows.
• Familiarity with cheminformatics or polymer informatics, molecular representations, descriptor engineering, RDKit, characterization‑informed modeling, multimodal data fusion, interpretable machine learning, natural language processing, text mining, or automated extraction of materials data from the literature.
• Experience with workflow automation, data infrastructure, database development, reproducible research pipelines, and collaborative environments that span computation, data science, and experiment.

Application Materials

• Updated CV/Resume.
• Unofficial Ph.D. transcripts.
• Copy of the Ph.D. diploma (if already awarded).

Job Details

• Job Family: Postdoctoral.
• Job Profile: Postdoctoral Appointee.
• Worker Type: Long‑Term (Fixed Term).
• Time Type: Full time.
• Expected hiring range: $72,879.00–$121,465.00.
• Comprehensive benefits are part of the total rewards package.

Equal Employment Opportunity Statement

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.