Organization Overview:
The Center for Functional Nanomaterials (CFN) at Brookhaven is a DOE-funded national scientific user facility, offering users a supported research experience with top-caliber scientists and access to state-of-the- art instrumentation. The CFN mission is advancing nanoscience through frontier fundamental research and technique development and is the nexus of a broad collaboration network. Each year, CFN staff members support the research of nearly 600 external facility users.
Three strategic nanoscience themes underlie the CFN scientific facilities: The CFN conducts research on nanomaterial synthesis by assembly designing precise architectures with targeted functionality by organizing nanoscale components. The CFN researches and applies platforms for state-of-the-art techniques for Accelerated Nanomaterial Discovery, integrating synthesis, advanced characterization, physical modeling, and computer science to iteratively explore a wide range of material parameters. The CFN develops and utilizes advanced capabilities for studies of Nanomaterials in Operando Conditions for characterizing materials and reactions at the atomic scale in real-world environments.
Position Description:
In this position, you will be a member of the Theory and Computation group working under the supervision of Sara Mason. You will play a key role in advancing the development of machine-learned interatomic potentials (MLIPs) for nanomaterials and applying them to model complex systems. Your work will involve close collaboration with scientists specializing in areas such as energy storage, catalysis, and geochemistry. You will investigate ultra-long timescale reaction dynamics using advanced quantum mechanical calculations, MLIPs, and problem-specific AI/ML approaches. This position offers the opportunity to work in an interdisciplinary team and contribute to cutting-edge research at the intersection of nanoscience and machine learning.
Essential Duties and Responsibilities:
You will perform molecular dynamics simulations using MILPs.
You will perform density functional theory (DFT) calculations for solid state materials.
You will build and train machine learning models to enhance materials research.
You will model the stability and transformations of nanomaterials under different conditions.
You will disseminate your results through publications and conference presentations.
Required Knowledge, Skills, and Abilities:
You are committed to fostering an environment of safe scientific work practices
You have earned a Ph.D. in chemistry, materials science, physics or a related discipline within the past five years and will complete your degree prior to the start date.
You have experience with density functional theory or molecular dynamics simulations.
You have experience in programming, particularly using Python.
Your research experience is demonstrated through publications, conference records, GitHub repository records, or other public communications.
Preferred Knowledge, Skills, and Abilities:
You have experience developing and applying machine learning models
You have experience in ab initio molecular dynamics.
You are familiar with CUDA programming and GPU-based computing.
You have experience with machine learning frameworks such as Pytorch or TensorFlow.
You communicate effectively, both verbally and through technical writing.
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 of the employment.
Brookhaven National Laboratory is committed to providing fair, equitable and competitive compensation. The full salary range for this position is $71900 - $80000/ year. Salary offers will be commensurate with the final candidate’s qualification, education and experience and considered with the internal peer group.
