The Applied Mathematics Group of the Computational Science Initiative (CSI) at Brookhaven National Laboratory (BNL) invites exceptional candidates to apply for a post-doctoral research associate position in applied mathematics, machine learning, and scientific computing. This position offers a unique opportunity to conduct research in emerging interdisciplinary research problems at the intersection of applied mathematics, machine learning, and high-performance computing (HPC) with applications in diverse scientific domains of interest to BNL and the Department of Energy (DOE). Topics of specific interest include: (i) optimal decision-guided information and data processing including data reduction; (ii) high-dimensional compositional workflows that involve machine learning (ML) models; (iii) Bayesian inference and uncertainty quantification in scientific ML models and physical systems; (iv) learning/optimization of low-dimensional latent feature spaces for ML surrogates. The position includes access to world-class HPC resources, such as the BNL Institutional Cluster and DOE leadership computing facilities. Access to these platforms will allow computing at scale and will ensure that the successful candidate will have the necessary resources to solve challenging DOE problems of interest.

This program provides full support for a period of two years at CSI with possible extension. Candidates must have received a doctorate (Ph.D.) in applied mathematics, statistics, computer science, or a related field (e.g., mathematics, engineering, operations research, physics) within the past five years. This post-doc position presents a unique chance to conduct interdisciplinary collaborative research in BNL programs with a highly competitive salary. 

Essential Duties and Responsibilities:

• Conduct research in various applied mathematics/machine learning problems in the context of compositional workflows and surrogate or reduced modeling.
• Work in interdisciplinary collaborations with applied domain scientists on various aspects of scientific data generation, processing, and compression.
• Formulate a high-quality research program in collaboration with mentors in the group.

Required Knowledge, Skills, and Abilities:

• Ph.D. in applied mathematics, statistics, computer science, or a related field (e.g., mathematics, engineering, operations research, physics) awarded within the last 5 years.
• Programming experience in scientific modeling, scientific computing, or machine learning.

Preferred Knowledge, Skills, and Abilities:

• Scientific machine learning / physics-informed machine learning, reduced modeling
• Decision theory, decision making under uncertainty, or optimal design of experiments
• Bayesian inference, uncertainty quantification, or spatiotemporal statistics
• Bayesian optimization, nonlinear/nonconvex optimization, mixed-integer programming
• Experience in scientific Machine Learning (ML) applied to domain sciences problems (e.g., in physical sciences, life sciences, or engineering)

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.

Brookhaven National Laboratory is committed to providing fair, equitable and competitive compensation. The full salary range for this position is $68400 - $113250 / year. Salary offers will be commensurate with the final candidate’s qualification, education and experience and considered with the internal peer group.

Brookhaven employees are subject to restrictions related to participation in Foreign Government Talent Recruitment Programs, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation at the time of hire for review by Brookhaven. The full text of the Order may be found at: https://www.directives.doe.gov/directives-documents/400-series/0486.1-BOrder-a/@@images/file