Requisition Id13456

Overview and Purpose:
The Integrated Computational Earth Sciences (ICES) Group in the Computational Sciences and Engineering Division (CSED) is seeking a Research Scientist in Computational Model-Data Integration to advance the integration and synthesis of observations and experiments into Earth system models across spatial and temporal scales. The successful candidate will apply multidisciplinary expertise and quantitative skills to develop and apply mathematical theory models and artificial intelligence (AI)/machine learning (ML) methods to the investigation and simulation of plant carbon water and nutrient interactions and conduct simulations and analysis to improve predictions under changing environmental conditions and disturbance. Research topics will include formulation of model representations of root structure and soil organic carbon phenology and function and photosynthesis at scale on high performance computing environments. CSED focuses on transdisciplinary computational science and analytics at scale to enable scientific discovery across the physical sciences engineered systems and biomedicine and health. It develops community applications data assets and technologies and provides assurance to build knowledge and impact in novel crosscut-science outcomes.

The candidate will be a staff scientist within the ICES Group of the Advanced Computing Methods for Physical Sciences Section in CSED. ICES is focused on the conduct of world-class research and development in Earth system modeling; model-data integration large scale data management analytics and machine learning; and model benchmarking at DOEs Leadership Computing Facilities (LCFs) and its National Energy Research Scientific Computing Center (NERSC). The CESG has specific strengths in numerical methods simulation and analysis focused on terrestrial biogeochemistry and hydrology atmospheric and ocean dynamics aerosols regional climate ice sheets and sea level rise and the global carbon cycle.

Major Duties/Responsibilities:

• Design and implement algorithms for hybrid process-/machine learning-based modeling and data analytics.
• Conduct simulations and analyses of plant and soil ecohydrology that connect plant community structure hydrodynamics nutrients carbon assimilation root allocation and physiology along with soil composition and biogeochemistry coupled with other Earth system model (ESM) components on various high performance computing platforms.
• Work with the research community to design and develop model evaluation metrics and to synthesize benchmark datasets for model evaluation.
• Collaborate with a diverse team of Earth system and computational scientists both within CSED and across DOE Labs partner universities and other federal agency sponsors.
• Publish research in peer-reviewed journals and agency reports and present results at national and international conferences.

Basic Qualifications:

• 4+ years of post-Ph.D. experience
• Ph.D. degree in computational science earth system science ecosystem ecology hydrology environmental engineering geography or a related field.
• Experience with modeling the terrestrial cycling of carbon water nutrients and energy.
• Ability to design ESM simulation protocols apply advanced statistical methods and machine learning to perform multi-objective optimization and assess uncertainty in model predictions.

Preferred Qualifications:

• Previous research experience with land surface models (e.g. ELM CLM) terrestrial ecosystem modules (e.g. FATES ED) soil biogeochemistry modules (e.g. CTC ECA FUN) and simulation protocols (e.g. AMIP CMIP6 C4MIP LUMIP LS3MIP).
• Knowledge of land-related observational data including manipulative experiments from in situ measurements and remote sensing platforms (e.g. Ameriflux FLUXNET AVIRIS-NG MODIS GEDI).
• Experience with FORTRAN C/C++ R and Python and with Linux Git and LaTeX.
• Familiarity and parallel programming experience with MPI OpenMP pthreads OpenACC CUDA and performance-portable programming models such as Kokkos Legion and HPX.
• Knowledge of common data file formats and conventions (e.g. CF netCDF HDF).
• Experience with high performance computing advanced statistical and machine learning methods and visual data analytics.
• Knowledge of terrestrial ecosystem processes landatmosphere interactions hydrological processes and terrestrialaquatic processes and their representations in ESMs.
• Strong motivation to conduct cutting-edge Earth system ecohydrology studies within multi-disciplinary teams.
• Ability to report regular progress and publicize results through contributions to manuscripts reports and conference presentations.
• Excellent verbal and written communication skills.

Relocation:

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This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

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