Brookhaven National Laboratory Post-doc - Model representation of leaf and canopy processes in tropical forests in Upton, New York
Post-doc - Model representation of leaf and canopy processes in tropical forests Job ID 1555 Date posted 12/06/2018
Brookhaven National Laboratory is a multipurpose research institution funded primarily by the U.S. Department of Energy's Office of Science. Located on the center of Long Island, New York, Brookhaven Lab brings world-class facilities and expertise to the most exciting and important questions in basic and applied science-from the birth of our universe to the sustainable energy technology of tomorrow. We operate cutting-edge large-scale facilities for studies in physics, chemistry, biology, medicine, applied science, and a wide range of advanced technologies. The Laboratory's almost 3,000 scientists, engineers, and support staff are joined each year by more than 4,000 visiting researchers from around the world. Our award-winning history, including seven Nobel Prizes, stretches back to 1947, and we continue to unravel mysteries from the nanoscale to the cosmic scale, and everything in between. Brookhaven is operated and managed by Brookhaven Science Associates, which was founded by the Research Foundation for the State University of New York on behalf of Stony Brook University, and Battelle, a nonprofit applied science and technology organization.
The Terrestrial Ecosystem Science and Technology (TEST) group in the Environmental & Climate Sciences Department at Brookhaven National Laboratory seeks to improve the representation of ecosystem processes in Earth System Models in order to increase our ability to understand and project global change. We study processes that have a global impact on climate, and focus on ecosystems that are poorly understood, sensitive to global change, and inadequately represented in models. Our goal is to advance process level understanding of terrestrial ecosystems, incorporate new knowledge into models, reduce model uncertainty and ultimately improve our ability to understand and project global change. We develop and use novel computational methods to quantify model sensitivity and target critical areas where improved process knowledge will reduce model uncertainty. Using state-of-the-art techniques - including remote sensing technologies - we advance mechanistic understanding to enable scaling of key ecosystem processes and then inform models iteratively through measurements and environmental manipulations.
The Terrestrial Ecosystem Science and Technology (TEST) group () is seeking a post-doc interested in improving understanding and model representation of leaf and canopy level processes that regulate the exchange of carbon, water and energy in tropical forests to enable improved projections of the response of tropical forests to climate change. The successful candidates will join a small, highly collaborative and supportive group at BNL and collaborate with other scientists as part of the multi-institutional Next Generation Ecosystem Experiments - Tropics project (ngee-tropics.lbl.gov). The post-docs will work under the joint supervision of Drs. Shawn Serbin and Alistair Rogers. This position is full time, 1-year appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds, and ongoing operational needs. We are now considering applications and the position will remain open until a suitable candidate has been identified. To ensure full consideration please apply before March 31, 2019.
Essential Duties and Responsibilities:
Evaluate alternative formulations and parameterization associated with representation of leaf and canopy processes (e.g. photosynthesis, stomatal conductance, photosynthetic seasonality), using tools such as the Multi-Assumption Architecture and Testbed (MAAT) modelling system, and the Predictive Ecosystem Analyzer (PEcAn);
Implement new formulations into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), which is linked to the Department of Energy's Earth System Model - Energy Exascale Earth System Model (E3SM); and
Publish results in peer-reviewed journals and present at scientific conferences.
BNL policy requires that research associate appointments be made to individuals who have received their doctorate within the past 5 years.
Required Knowledge, Skills, and Abilities:
A Ph.D. in plant biology, environmental or climate science, or a closely related field (if not already graduated, Ph.D must be expected early in 2019););
Effective written and oral communication skills;);
A record of publication in high quality internationally recognized journals; and);
Ability to work in a team environment.
Preferred Knowledge, Skills, and Abilities:
Experience working in a high performance computing environment.
Ability to integrate and run existing process models with uncertainty quantification.
Experience in computer programming and modifications to existing model code.
The development, testing, and application of sub models of carbon uptake and respiration.
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:
Paid Parental Leave
Swimming Pool, Weight Room, Tennis Courts, and many other employee perks and benefits
We invite you to consider Brookhaven National Laboratory for employment. To be considered for this position, please apply online at BNL Careers and enter the job title into the Keyword Search.
Brookhaven National Laboratory (BNL) is an equal opportunity employer 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 affirmative action in support of its policy and to advance in employment individuals who are minorities, women, protected veterans, and individuals with disabilities.
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