A postdoctoral position on the AI/machine learning and data analysis of materials is available immediately at the Center for Nanoscale Materials (CNM) at Argonne National Lab (near Chicago, USA).

The postdoctoral researcher will work on developing of AI and machine learning workflows and tools to generate non-equilibrium phase diagrams of materials.  Methods will include molecular dynamics, deep neural networks, reinforcement learning, data mining, genetic algorithms.

Applicants should have considerable knowledge of the following:

• Statistical Mechanics, Thermodynamics (Free energy calculations)
• Atomistic and/or first principles simulation (classical and ab-initio molecular dynamics, DFT, simulations)
• Numerical optimization methods (e.g. machine learning including deep neural networks, reinforcement learning, data mining, genetic algorithms, clustering techniques etc.)

Good skills in (1) data analysis and/or scientific visualization (e.g. feature detection and tracking of high-level structures, classification, statistical summaries, comparisons between models and experiments), (2) developing empirical force fields, and (3) developing scientific software are advantageous. 

The postdoctoral researcher will have the opportunity for extensive collaborations with industrial collaborators as well as various experimental and computational groups at CNM and in the Mathematics and Computer Science division at Argonne.  The postdoctoral researchers will work in a dynamic team of staff scientists at Argonne National Laboratory (Subramanian Sankaranarayanan, Pierre Darancet, Mathew Cherukara, Ian Foster, Logan Ward). Within the team we have extensive experience with molecular dynamics simulations, first principles calculations, reactive empirical force fields, chemical dynamics, deep learning and numerical algorithms, data analysis, experimental characterization and imaging. Our research has involved methodology and algorithm development in conjunction with extensive applications in the fields of nanoscience and energy-related materials.  

Interested candidates should direct questions or apply by emailing the following information to Dr. Subramanian Sankaranarayanan ([email protected]) :

1) A detailed curriculum vitae including a list of publications and the names and email addresses of three professional references.
2) One representative publication that best showcases your work 
3) A cover letter answering the following questions: 

• What materials modeling technique(s) do you have expertise in?
• What is your experience with software development (if any)?
• What is your experience with AI/ML methods (if any)?
• What is your experience with collaborative projects?
• What is your expected start date and US work eligibility status (if known)?

Review of applications will begin immediately and continue until the position is filled. The successful candidate will be offered a competitive package, commensurate with qualifications and experience. Initial appointment will be for a period of one year and will be renewable for two additional years. Applicants must have received their PhD within the last 5 years.

Argonne National Laboratory is an equal opportunity employer, and we value diversity in our workforce.