The Theory and Modeling Group at the Center for Nanoscale Materials at Argonne National Laboratory has an immediate opening for a staff at the level of Assistant Scientist in the broad area of machine learning and AI for materials design and discovery at the Nanoscale. Details of the position are below.

Interested candidates can apply at:

https://careers.peopleclick.com/careerscp/client_argonnelab/external/gateway.do?functionName=viewFromLink&jobPostId=9875&localeCode=en-us

Please contact Dr. Subramanian Sankaranarayanan ([email protected]) if you have any questions.

Basic Purpose

Conducts basic research in computational nanoscience, data science, artificial intelligence, machine learning and high performance computing, working across the Nanoscience & Technology and in collaboration with other divisions within the Physical Sciences Directorate as well as the Mathematics and Computer Science Divisions at Argonne. Research efforts include, but are not limited to, areas such as development and application of new AI/ML algorithms applied to materials science and chemistry, scientific applications in computational nanoscience, development of data infrastructure to enable nanoscience, and theory of nanostructured materials. Expertise in high performance computing (HPC) administration and HPC operations/maintenance is highly desirable. Also performs basic and translative research in support of the Center for Nanoscale Materials user science community.

Responsibilities

Conducts both independent and collaborative research with Argonne scientists relevant to the US Department of Energy (50%). Publishes results of independent and collaborative research in refereed journals and makes presentations at meetings, conferences, symposia and seminars. Assists in proper execution and reporting of projects for CNM, DOE and other outside sponsoring sources.

Establishes and maintains a vibrant and productive collaborative program with CNM users (50%). Stays abreast of progress in pertinent fields and to assist the CNM and user community. Supports end-users with HPC operations/maintenance issues using the cluster, optimizing job submissions, getting a full understanding of workflows, and software installation support.

Provides work direction to postdoctoral appointees, research assistants, students and technical staff working on projects within their area of responsibility.

Works toward achieving promotion to grade RD3.

Candidate may be required to manage vendor relationships as it pertains to hardware, cloud, and managed support services.

Safety, Security, and Environmental Protection: All activities, as they apply to work performed by self or by personnel under supervision, will be executed in compliance with ES&H and security responsibilities established by Argonne National Laboratory's ES&H policies, Safeguards and Security policies, work rules, and safe practices.

Knowledge, Skills and Experience

Minimum Education/Experience Requirements:

Bachelor's Degree and 5+ years work experience, or Master's Degree and 3+ years work experience, or Doctorate and 0 years or their equivalents.

Additional Requirements:

Considerable :
-Considerable knowledge of machine learning, artificial intelligence, and data science applied to materials science and/or nanoscience.

--Considerable skill in formulating scientific problems in the design and theory of nanoscale systems for applications relevant to the DOE portfolio.

-Considerable skill in formulating scientific issues, data management, and high performance computing.

-Considerable oral and written communications skills and the ability to work with internal and external contacts and achieve established goals.

Good :
-Good skill in understanding abstract concepts and synthesizing results within the current experimental and theoretical framework.

-Good skill in designing high-quality experiments and/or theories to address relevant scientific issues.

-Good knowledge of additional areas of chemistry, physics, and materials science.

Other :
The knowledge and skills required to function in this position would typically be achieved through a PhD in one of the basic sciences or engineering with educational or experiential focus on nanoscience.

This position description documents the general nature and level of work but is not intended to be a comprehensive list of all activities, duties and responsibilities required of job incumbent. Consequently, job incumbent may be required to perform other duties as assigned.