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 experiments. In 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.

• 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.

• 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.

• You have experience deriving and interpreting physically meaningful descriptors from simulation data to rationalize structure-property or structure-reactivity 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.

• 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 $71,900 - $82,400 per year. Salary offers will be commensurate…