We have two open postdoctoral positions in the Combustion Research Facility (CRF) at Sandia National Laboratories in Livermore, CA. One of them is in the field of theoretical heterogeneous catalysis (Job ID 675471), and the other one is in atomistic machine learning (Job ID 675905). The description of the jobs can be found below.

To Apply:

• go to http://www.sandia.gov/careers/students_postdocs/postdocs.html
• click 'View All Jobs'
• search for Job IDs 675471 and 675905

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Job ID 675471: Theoretical Heterogeneous Catalysis

A postdoctoral fellowship is available immediately to work under the supervision of Dr. Judit Zádor at Sandia National Laboratories in Livermore, California. The postdoctoral  associate will be primarily responsible for the development of our code “pynta” (https://github.com/zadorlab/pynta), which enables large-scale kinetics calculations for heterogeneous catalytic systems using high performance computing. The work involves coding mostly in Python, hands-on experience with current peta- and upcoming exascale machines, and exploration of the mechanistic details of catalytic processes on surfaces. It also requires creativity, possibly combined with machine learning approaches, to come up with efficient and scalable algorithms for the exploration of potential energy surfaces in this domain. The work is in the framework of the Exascale Catalytic Chemistry (ECC) project (https://ecc-project.sandia.gov/), funded by the Department of Energy, and involves a strong collaboration with the members of our interdisciplinary and multi-institutional team, with the possibility of contributing to other aspects of the project as well.

QUALIFICATIONS WE REQUIRE

• A PhD in Chemistry, Chemical Engineering, Physics, Material Science, or related fields conferred within the past five years.
• Substantial expertise in an area of heterogeneous catalysis, computational chemistry and in scientific programming.
• Strong publication record matching the current career stage.
• Solid knowledge of Python.

QUALIFICATIONS WE DESIRE

• Good presentation, writing, and communication skills.
• Knowledge of the theoretical and modeling aspects of heterogeneous catalysis.
• Experience with atom-centered and plane wave quantum chemistry codes.
• Experience with high-performance computing.
• Familiarity with machine learning techniques.

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Job ID 675905: Atomistic Machine Learning

We are seeking candidates with experience in atomistic machine learning simulations and/or probabilistic machine learning methods development, to work on related science projects. The ultimate goal of the work is to gain fundamental understanding into atomistic processes about reaction probabilities but has a considerable code writing aspect to it to develop unique computational tools enabling the science goals. Therefore, strong scientific computing skills, including code development and experience with high-performance computing environments are needed to fill this position. The project includes the study of gas-phase processes. The work is to be carried out in an interdisciplinary team.

QUALIFICATIONS WE REQUIRE

• PhD in a field of chemical/physical sciences within five years prior to employment
• Knowledge and expertise in a domain of atomistic simulations, e.g., catalysis, solid-state physics, gas-phase chemistry, evidenced by publications
• Knowledge and expertise in computational science and software development, evidenced by code. The preferred language is Python.
• Good writing and communication skills

QUALIFICATIONS WE DESIRE

• The use of state-of-the-art machine learning tools in the physical sciences
• Knowledge and expertise in C++
• Experience in performing collaborative research
• Excellent interpersonal skills