A postdoctoral position on exascale atomistic simulations, AI/machine learning and data analysis of ferroelectric devices is available immediately at the Center for Nanoscale Materials (CNM) at Argonne National Lab (near Chicago, USA). The postdoctoral researcher will work on the development of large-scale molecular dynamics, AI and machine learning based analysis to understand ferroelectric device operation. The project involves large-scale simulations on exascale computing resources to probe switching behavior while accounting for effects of defects, competing metastable phases, doping concentration, grain boundaries, domain wall mobility and several other nano-to-mesoscale interfacial effects.

The postdoctoral researcher will have the opportunity for extensive collaborations with industrial collaborators as well as various experimental and computational groups at CNM, at Advanced Leadership Computing Facility (ALCF) and the Computational Science Division (CPS) division at Argonne.

The postdoctoral researchers will work in a dynamic team of staff scientists at Argonne National Laboratory. Within the team we have extensive experience with large scale 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.

Position Requirements

Applicants should have considerable knowledge of the following:

• a PhD in physics, or closely related field. Degree must have been received within the last 5 years or upcoming year
• Experience with large-scale molecular dynamics (MD) simulations using software such as LAMMPS. Experience in handling and data analysis generated from multi-million to multi-billion atom MD simulations.
• Proficiency with data visualization tools.
• Understanding of force fields and interatomic potentials for ferroelectric and related materials.
• Atomistic and/or first principles simulation (classical and ab-initio molecular dynamics, DFT, simulations)
• Proficiency in programming languages like Python, C++, or Fortran for custom analysis tools. Experience in developing workflows that integrate simulation, machine learning, and data analysis.
• Numerical optimization methods (e.g. machine learning including deep neural networks, reinforcement learning, data mining, genetic algorithms, clustering techniques etc.)
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

Considerable skills in:

• Data analysis and/or scientific visualization (e.g. feature detection and tracking of high-level structures, classification, statistical summaries, comparisons between models and experiments),
• Developing empirical force fields
• Developing scientific software and workflows
• Experience in programming for HPC environments, including MPI, OpenMP, or CUDA are advantageous.

Interested candidates should apply via Workday and include the following with their application:

(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 large scale atomistic simulations and analysis?

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)?

Questions about the position can be directed to

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.

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