Organizational Overview

The Environmental & Climate Sciences Department (ECSD) (www.bnl.gov/envsci) at Brookhaven National Laboratory focuses on a wide range of theoretical experimental and field studies in support of the U.S. Department of Energy's climate and energy research agendas. Work is supported by the Atmospheric Radiation Measurement (ARM) User Facility (www.arm.gov) an observations-based program that measures aerosols clouds precipitation radiation and atmospheric dynamics; the Atmospheric Systems Research Program (https://asr.science.energy.gov/) and the Environmental System Science Program (https://ess.science.energy.gov/). This work is pursued with the ultimate goal of improving predictability and reducing the uncertainty in global and regional climate models. The department research portfolio also includes development of environmental technologies and applications for renewable energies urban science and national security.

The Computational Science Initiative (CSI) (https://www.bnl.gov/compsci/) provides a laboratory-wide umbrella for computational modeling and data science among other 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. CSIs Applied Mathematics group conducts research on a variety of applied science and engineering problems involving methods such as optimal experimental design uncertainty quantification decision making under uncertainty modeling and simulation surrogate and reduced order modeling extreme-scale data analysis and scientific machine learning.

Position Description
The Environmental and Climate Sciences Department seeks a Postdoctoral Research Associate to work closely with the Computational Science Initiatives Applied Mathematics Group on coupled aerosol/land-atmosphere models for optimal experimental design via coupled model-data integration uncertainty quantification and observing system simulation experiments. The successful candidate will have a strong interest in atmospheric and/or land-surface process modeling and in the climate science application of artificial intelligence/machine learning techniques. This position has a high level of interaction with an international and multicultural scientific community.

Current model-data operating paradigms for climate prediction rely on cumbersome and ad-hoc approaches to both observing system design and the assimilation of observing system data into models. BNLs new Four-dimensional Atmospheric Sensing and Simulation (FASSt) Initiative aims to establish a new observational and modeling paradigm that directly addresses large uncertainties in climate prediction with new model-guided observational requirements and approaches (FASSt-Sensing) coupled with advanced computational methods (FASSt-Simulation) defining optimal observation networks and rapid model-data assimilation. In this project we are leveraging our combined expertise in coupled land-atmosphere/aerosol systems (ECSD) and advanced computational methods (CSI) to build a novel adaptable and scalable computational framework for rapid model-measurement assimilation leading to advances in climate model predictability shortened timelines between observation and discovery and optimal observational network design.

Essential Duties and Responsibilities:

• Model-data integration and parameter estimation
• Evaluation of model calibration on predictive uncertainties
• Optimization of observational systems to reduce identified model uncertainties
• Publish in peer-reviewed journals
• Present at national and international meetings

Position Requirements
Required Knowledge Skills and Abilities:

• Ph.D. in atmospheric science ecosystems science aerosol science or a closely related field
• Experience with using atmospheric and/or land-surface process models and model-data integration
• Clear and concise verbal and written communication and collaboration skills
• Ability to develop and pursue independent research ideas
• Ability to work well in a diverse team across cultures and experiences

Preferred Knowledge Skills and Abilities:

• Computational capabilities for developing Optimal Experimental Designor other optimization solutions
• Climate science applications of artificial intelligence/machine learning techniques
• Uncertainty quantification
• A record of publication in high quality internationally recognized journals

Other:

• 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.
• Review of applications begins immediately. Applications will be accepted until the position is filled.
• The initial term appointment will be one year subject to renewal contingent on funding andperformance.
• Research will be under the direction of Drs.Chongai Kuang (ECSD) Shawn Serbin (ECSD) and Nathan Urban (CSI)

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)