Amalie Emmy Noether Fellowship in applied mathematics and scientific computing at Brookhaven National Laboratory

I would like to advertise the following 2-year postdoctoral fellowship in applied mathematics and scientific computing at Brookhaven National Laboratory, a U.S. Department of Energy research institution on Long Island, New York. This is not a Julia-specific position, but Julia experience would be a plus, so I thought I would post it here.

Post-Doc Applied Math / Scientific Computing Description at Brookhaven National Laboratory

Job ID: 2456
Date posted: 12/13/2020

Brookhaven National Laboratory ( delivers discovery science and transformative technology to power and secure the nation’s 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 Energy’s (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.

Organizational Overview

Brookhaven National Laboratory (BNL) is a scientific, extreme scale Data Laboratory in the U.S., New York State, Long Island. We have a lively, fast-growing data science research program at BNL, with a specific focus on the challenges presented by the analysis, interpretation, and use of data at extreme scales and in real-time. The data science program is accompanied by significant computational modeling research effort, in support of the design, planning, analysis, and interpretation of both laboratory and computer experiments and their results. The Computational Science Initiative (CSI - provides a laboratory-wide umbrella for these activities, bringing together computer scientists, applied mathematicians, and domain scientists to carry out leading-edge research, convert research results into practical solutions that advance domain science, and provide the necessary computing infrastructure services and training to support efficient operation. The position will reside within CSI’s Applied Mathematics group.

Position Description

The Applied Mathematics Group of the Computational Science Initiative (CSI) at Brookhaven National Laboratory (BNL) invites exceptional candidates to apply for the Amalie Emmy Noether Fellowship in applied mathematics and scientific computing. This fellowship offers a unique opportunity to conduct research in a broad set of fields, including reduced order modeling, uncertainty quantification and scalable computational statistics for Bayesian inference, optimization and control for decision making under uncertainty, scientific machine learning, high-dimensional inverse problems, multiscale modeling, integrated computational modeling frameworks, data science for streaming or “in-situ” (within simulation) analytics in high performance computing (HPC), and numerical methods. The methods and fundamental advances made in the course of this research will further the progress of applications of interest to BNL and the Department of Energy (DOE). Examples of such applications might include: data-driven uncertainty quantification and hybrid process-based/data-driven modeling for climate prediction and resilience planning, optimal experimental and simulation design for drug discovery and materials science, and large-scale data processing for particle accelerator experiments. The followship 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. Candidates must have received a doctorate (Ph.D.) in applied mathematics or a related field (e.g., mathematics, physics, engineering, statistics, operations research, or computer science) within the past five years. This fellowship presents a unique chance to conduct interdisciplinary collaborative research in BNL programs with a strongly competitive salary. Recipients will be allowed to select a direct mentor from a list of CSI staff scientists. This mentor will help the recipient define and pursue their own research agenda during their appointment.

Essential Duties and Responsibilities:

  • Conduct research in applied mathematics
  • Work in interdisciplinary collaborations with other mathematicians and applied domain scientists
  • Formulate an independent, high-quality research program in collaboration with lab mentors

Position Requirements

Required Knowledge, Skills, and Abilities:

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

Preferred Knowledge, Skills, and Abilities (one or more of):

  • Bayesian inference, predictive uncertainty quantification, or spatiotemporal statistics
  • Reduced order modeling, surrogate modeling, or model emulation
  • Scientific machine learning / physics informed machine learning
  • Optimization (e.g., nonlinear/nonconvex optimization, mixed-integer programming)
  • Decision theory, decision making under uncertainty, or optimal design of experiments
  • Modeling and simulation, multiscale modeling, data-model fusion
  • Numerical methods (e.g., partial differential equations, linear algebra, integration, inverse problems, randomized / low-rank / sparse algorithms for high-dimensional problems)
  • Scalable, parallel/distributed computational methods for high-performance computing
  • Software architecture for mathematical modeling frameworks
  • Experience solving applied domain sciences problems (e.g., in physical sciences, life sciences, or engineering)
  • Experience working in interdisciplinary collaborations

BNL policy requires that research associate appointments be made to individuals who have received their doctorate within the past five years.

At Brookhaven National Laboratory we believe that a comprehensive employee benefits program is an important and meaningful part of the compensation employees receive. Our benefits program includes, but is not limited to:

  • Medical, Dental, and Vision Care Plans
  • Flexible Spending Accounts
  • Paid Time-off and Leave Programs (vacation, holidays, sick leave, paid parental leave)
  • 401(k) Plan
  • Flexible Work Arrangements
  • Tuition Assistance, Training and Professional Development Programs
  • Employee Fitness/Wellness & Recreation: Gym/Basketball Courts, Weight Room, Fitness Classes, Indoor Pool, Tennis Courts, Sports Clubs/Activities (Basketball, Ping Pong, Softball, Tennis)

Brookhaven National Laboratory (BNL) is an equal opportunity employer that values inclusion and diversity at our Lab. We are committed to ensuring that all qualified applicants receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, status as a veteran, disability or any other federal, state or local protected class.

BNL takes an affirmative action in support of its policy and to advance in employment individuals who are minorities, women, protected veterans, and individuals with disabilities. We ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

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Brookhaven employees are subject to restrictions related to participation in Foreign Government Talent Recruitment Programs, as de!ned 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: