The Theory and Modeling group at the Center for Nanoscale Materials, Argonne National Laboratory, has immediate Research & Development Level 1 term staff opening in the area of computational materials modeling, AI/machine learning and data analysis of complex fluids. Specifically, the research involves development of AI and machine learning workflows and tools to understand critical phenomena in complex fluids. The researcher will have the opportunity for extensive collaborations with industrial collaborators as well as various experimental (NST, CSE and APS) and computational groups at NST and MCS divisions at Argonne. The position will work in a dynamic team of staff scientists at Argonne National Laboratory. 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. The project will also involve significant interactions with industry and is cross-divisional collaboration between the NST, CSE, and MCS divisions at Argonne. Please note that only applications through Argonne website will be considered. Questions about the position should be directed to Dr. Subramanian Sankaranarayanan (skrssank@anl.gov) Position Requirements Position Requirements: A Bachelor or Master's degree in a related field Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. The applicants should have strong programming skills and expertise in at least two or more of the following fields: Statistical Mechanics, Thermodynamics (Free energy calculations) Numerical methods and optimization (e.g. machine learning including deep neural networks, reinforcement learning, data mining, genetic algorithms, clustering techniques etc.) Data analysis and/or scientific visualization (e.g. feature detection and tracking of high-level structures, classification, statistical summaries, comparisons between models and experiments) Experience in software development and workflows is highly desirable Experience with atomistic or first principles simulation (DFT and ab-initio molecular dynamics simulations) of a wide variety of materials systems, Experience in developing empirical force fields for classical molecular dynamics of clusters, surfaces and interfaces will be considered an advantage. A PhD in theoretical physics/ chemistry, computational materials science, chemical engineering, applied mathematics or a related field is preferred. Applicants must have received their PhD within the last 5 years 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 RD1: Bachelor’s and 0 years or Master’s and 0 years Job Family Research Development (RD) Job Profile Computational Science 1 Worker Type Long-Term (Fixed Term) Time Type Full time As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. 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