Brookhaven National Laboratory · 4 days ago
Electronic Structure and Data-Driven Materials Design
Upton, NY
Full-time
Onsite
New Grad, Entry Level
$72K/yr - $82K/yrBrookhaven National Laboratory is a multidisciplinary laboratory committed to discovery science and transformative technology. The role involves conducting first-principles simulations and data-driven analyses to understand and design catalytic materials, while working closely with experimental collaborators and contributing to the development of reusable computational workflows.
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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
Qualification
Required
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
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
Benefits
Comprehensive employee benefits program
Company
Brookhaven National Laboratory
Brookhaven National Laboratory is a multi-purpose research institution focused on questions in basic and applied science.
Founded in 1947
1001-5000 employees
H1B Sponsorship
Brookhaven National Laboratory has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (123)
2024 (117)
2023 (104)
2022 (84)
2021 (98)
2020 (98)
Funding
Current Stage
Late StageTotal Funding
unknownKey Investors
US Department of Energy
2022-09-19Grant
Leadership Team
Thomas Schlagel
Advisor
Recent News
2026-02-03
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