Electronic Structure and Data-Driven Materials Design

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In this position you will conduct first-principles simulations and data-driven analyses to understand and design catalytic materials with targeted redox and adsorption behavior. You will combine density functional theory thermodynamics and automated Python-based workflows to generate physically grounded datasets describing oxidation states defect formation and surface reactivity under realistic conditions. A central aspect of the role is the derivation of interpretable descriptors from electronic structure calculations and the application of machine-learning methods (e.g. Random Forest Trees and related ensemble models) to identify key controls governing catalytic performance. You will work closely with experimental collaborators to interpret spectroscopic and catalytic measurements and to guide future addition this position will contribute to the development of reusable computational workflows data products and analysis approaches that support broader data-enabled research activities at the Center for Functional Nanomaterials including potential integration with user-facing data services and facility-scale analysis pipelines.

Essential Duties and Responsibilities:

You will perform first-principles electronic structure and surface thermodynamics calculations to model redox processes adsorption and defect stability in catalytic materials.
You will develop and use Python-based automated computational workflows for simulation setup execution on HPC resources and systematic post-processing of results.
You will derive physically interpretable electronic geometric and thermodynamic descriptors from simulation data and apply machine-learning methods (e.g. Random Forest Trees and related approaches) to identify governing trends.
You will use computational spectroscopy and electronic structure analysis to interpret and rationalize experimental measurements working closely with experimental collaborators.
You will contribute to the development of reusable workflows analysis tools and data products that support data-enabled research and evolving user-facing data services at the Center for Functional Nanomaterials.

Required Knowledge Skills and Abilities:

You have a Ph.D. in a relevant discipline (Materials Science Physics Electrical Engineering or a related engineering discipline) conferred within the past five years or to be completed prior to the starting date.
You have experience modeling chemically non-trivial electronic structure such as mixed or non-integer oxidation states redox-active materials defect states or unconventional bonding environments using first-principles methods.
You have experience using Python for scientific computing including data analysis automation or workflow development.
You have experience applying machine-learning or statistical methods (e.g. Random Forest Trees gradient boosting or related approaches) to analyze scientific datasets.
You have experience working in a high-performance computing (HPC) environment including job submission and management of computational workloads.
You are committed to fostering an environment of safe scientific work practices.

Preferred Knowledge Skills and Abilities:

You have experience deriving and interpreting physically meaningful descriptors from simulation data to rationalize structureproperty or structurereactivity relationships.
You have experience with computational spectroscopy using electronic structure calculations to interpret or rationalize experimental spectroscopic measurements (e.g. vibrational electronic magnetic or core-level spectroscopy).
You have experience applying LLM-based tools for literature-informed data extraction within computational workflows.
You have familiarity with modeling solvent effects and environmental conditions such as implicit solvation temperature or gas-phase chemical potentials in computational studies.
You work effectively in a collaborative interdisciplinary research environment and communicate clearly through technical writing presentations and well-documented code.

Other Information:

This is a 2-year Postdoc Assignment.

BNL policy requires that after obtaining a PhD eligible candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience as a post- doc and/or in an R&D position excluding time associated with family planning military service illness or other life-changing events.

Candidates must have completed all degree requirements by the commencement.

Brookhaven National Laboratory is committed to providing fair equitable and competitive compensation. The full salary range for this position is $71900 - $82400 / year. Salary offers will be commensurate with the final candidates qualification education and experience and considered with the internal peer group.

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Brookhaven National Laboratory () delivers discovery science and transformative technology to power and secure the nations future. Brookhaven Lab is a multidisciplinary laboratory with seven Nobel Prize-winning discoveries 37 R&D 100 Awards and more than 70 years of pioneering research. The Lab is primarily supported by the U.S. Department of Energys (DOE) Office of Science. Brookhaven Science Associates (BSA) operates and manages the Laboratory for DOE. BSA is a partnership between Battelle and The Research Foundation for the State University of New York on behalf of Stony Brook University. BSA salutes our veterans and active military members with careers that leverage the skills and unique experience they gained while serving our country learn more at BNL Opportunities for Veterans at Brookhaven National Laboratory.

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