[ES_JOBS_NET] Machine Learning Scientist, NCAR CISL

[David Gagne]

The Machine Learning (ML) scientist will apply ML to the Earth system
sciences as part of the Analytics and Integrative Machine Learning (AIML)
Group in the Technology Development Division (TDD) in the Computational and
Information Systems Laboratory (CISL) at the National Center for
Atmospheric Research (NCAR). The incumbent will have a proven ability to
apply a variety of ML algorithmic approaches to problems in earth systems
science or related physical science disciplines.

Relevant ML application experience may include the use of ML training and
inference systems for the recognition, prediction or tracking of important
features or events in datasets, or alternatively, through the auto-encoding
of suitable physics parameterizations in earth system models with neural
networks, the replacement of model components with efficient, learned
emulators. Machine learning techniques may also be applied by the
scientist to automate or accelerate the human data analysis of hundreds of
routine data products, thus amplifying the scientific capability of
researchers, the integration of non-traditional data sources into earth
system prediction systems, to help optimize supercomputing workflows
through ML-guided resource management, or for the early detection and
steering of numerical simulations.


Responsibilities:

The position works on machine learning projects with scientists, engineers
and students in NCAR’s Computational and Information Systems Laboratory
(CISL), the Research Applications Laboratory (RAL), the Earth Observing
Laboratory (EOL), the High Altitude Observatory (HAO), and the Atmospheric
Chemistry, Observations and Modeling (ACOM) Laboratory, and potentially
with external data scientists as well. The successful candidate will
identify and apply appropriate machine learning techniques focused on two
initial projects:


   - Emulating atmospheric organic chemistry reaction networks. Current
   chemistry-climate models cannot represent the complex chemistry involved in
   the degradation of hydrocarbons emitted by anthropogenic activities, due to
   the enormous number of species and reactions. ML emulation may produce less
   costly reduced models, providing new opportunities to investigate their
   impact on human health and air quality.


   - Data bottlenecks in cloud observational systems. Estimating the
   radiation balance in the Earth system is central to predictive climate
   models and hinges on understanding cloud processes.  The Holographic
   Detector for Clouds (HOLODEC) is an airborne instrument that gives an
   unrivaled view of 3-D distributions of droplets, providing an unprecedented
   accuracy and detail of cloud physics data. However, analyzing the huge data
   volumes produced by the instrument with current techniques presents a
   bottleneck, limiting the instrument’s scientific utility. Image-based ML
   methods could accelerate the analysis process and help advance our
   understanding of cloud processes.

For each project, the scientist will work with the team, and in
collaboration with domain scientists, to create and share the necessary
training datasets, and apply, tune and evaluate and verify a variety of
machine learning approaches to solving these problems. The machine learning
scientist’s efforts will be built on top of NCAR’s core capability in
domain-focused statistical development, and will leverage its vast
observational and model output datasets, and CISL’s petascale
supercomputing infrastructure, and cloud-based resources and environments.
The position will require the ability to work in teams and across
disciplines in order to cross-fertilize ideas and build strong
collaborations to tackle Earth system science challenges. This integration
with and support from colleagues in the earth system sciences will help to
ensure the relevance and sustainability of the ML project scientist’s
research activities.

The position provides high-level machine learning expertise to these
projects, assists in planning the projects human and financial resource
requirements, and will participate in the evaluation of the project’s
progress, its results, and make adjustments to the project’s approach to
better achieve objectives. The Machine Learning scientist may also serve,
from time to time, as a consultant to internal staff and external
organizations on machine learning topics.


See the full job ad and apply at
https://ucar.wd5.myworkdayjobs.com/en-US/UCAR_Careers/job/Mesa-Lab-Building/Machine-Learning-Scientist_REQ-2019-39-1.



David John Gagne
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