CIRES/ EARTH LAB Machine Learning Applications to Earth Observations of Natural Hazards Post-Doc

Earth Lab, funded by the University of Colorado Boulder’s “Grand Challenge: Our Space, Our Future” and part of CIRES, seeks post-doctoral researchers to join a dynamic team pushing the frontiers of coupled Earth and social system science ( Earth Lab’s mission is to harness the data revolution through research, analytics, and education to accelerate understanding of global environmental change to help society better manage and adapt.


Earth Lab is seeking a Post-Doctoral Research Scholar to lead a research agenda in the following area:  Machine Learning Applications to Earth Observations of Natural Hazards. This targeted research area represents Earth Lab’s efforts to explore natural and social system vulnerability and resilience to global environmental change, while also capitalizing of the diversity of data available to generate new insights. All postdoc positions are for one year with the possibility of extension based on performance and funding availability.


Earth Lab seeks a Postdoctoral Research Scholar to advance machine learning applications in analyzing and integrating Earth observations, and non-traditional data sources - like Twitter or Zillow - to better understand global change and the consequences to ecosystems and society. This candidate will push forward on research frontiers that are emerging as methodological tools from the deep learning revolution spillover into the Earth and environmental sciences. These include applications of generative models, generative adversarial networks, self-supervised or semi-supervised learning for sparse data, transfer learning, reinforcement learning, and science-based deep learning that combines dynamical or physical models with neural networks. The candidate chosen for this position would have flexibility in terms of the particular systems, questions, and machine learning methods that are studied. Example research topics include:

Benefits of super-resolution techniques applied to satellite imagery assessed by classification accuracy, unmixing ratios, and/or physical process modeling. 

Combining co-located point cloud data (e.g., structure from motion) with RGB, multispectral and thermal imagery for surface characterization.

Integration of commercial satellite imagery with long-term NASA and USGS research satellite observations (off-nadir view angles, inconsistent radiometry)

Real-time disaster response support that integrates disparate streams of observations including remotely sensed and social media data. 

Science-based deep learning that permits inference about dynamical models and facilitates prediction.

Reinforcement learning to evaluate optimal control of wildfires.

Self-supervised learning for plant classification from hyperspectral imagery.

Scalable Bayesian deep learning for earth science applications that accommodates parameter uncertainty (e.g., variational inference).

The research goal of this work is to advance our understanding of some aspect of:

Predictive inference in support of real time, short term, and/or long term prediction. 

Object detection, segmentation, or classification of remotely sensed observations that leverages convolutional neural networks with limited training data.

Development of key Earth science training datasets to support future work.

Use of cloud computing resources to scale analytics.

Natural hazards, extreme events, and global change using multiple streams of information.


Work  with  individual  team  to  meet  research  goals  of this position, with the expectation of submitting  one  manuscript  for  publication  to  a  peer-reviewed  journal  by  the  end  of  Year  1, and contribute  to  and/or  submit an  independent  funding  proposal.

Use  research  agenda  to  develop  use  cases  for  the  Analytics  Hub  (staff  and Viz-studio)  with  heterogeneous  and/or  big  data  streams  that  require  specialized  data  management,  analysis,  and/or  visualization  support  (with  emphasis  on  data  from  the  Earth  Observation  enterprise,  from  ground-based  to  space-based  sensors).

Contribute to  discussions  with  industry,  federal,  and  other  academic  partners, and contribute to  opportunities  to  make  Earth  Lab  visible  in  the  community.

Contribute to the open, reproducible science objectives of Earth Lab.  This could  include  contributing  or  publishing  well-documented  data sets  or  code  recipes  that  could  serve  multiple  users  within  Earth  Lab  or  other  audiences.

Work  on  synergistic  activities  across  the  post-doc  cohort  and  Science  Teams.  This  could  include  collaboration  on  external  proposals,  papers,  workshops,  opportunities  with  industry/federal  partners,  or  other  collaborative  activity and should amount to 20% effort.

What You Should Know

This position is located at the University of Colorado at Boulder's main campus. 

All postdoc positions are for one year with the possibility of extension based on performance and funding availability.

To learn more about Earth Lab, visit Earth Lab’s website ( and Earth Lab’s learning portal (

Review of applications will begin October 18. The position will remain open until filled.

What We Can Offer

We can offer competitive salary and a comprehensive benefits package.


Ph.D. in a related field is required, such as applied mathematics, computer science, geography, ecology, environmental studies, remote sensing, or other.

Background expertise in machine learning, deep learning, artificial intelligence, and/or statistics possibly with applications to environmental data.

Applicant must have demonstrated interest and skills in one or more of the approaches described above (e.g., machine learning approaches, remote sensing, data integration across multiple sources, etc.).

A strong quantitative background is necessary.

The ability to work as part of an interdisciplinary team.

What You Will Need

Experience in, or willingness to learn, appropriate programming and data analytic tools. Ideally the candidates will have experience in programming languages (e.g., R, Python, or others), can work in different environments (e.g., Linux), and are well versed in geospatial analysis software (e.g., QGIS).

Demonstrated contributions to open science (i.e., publicly available and/or reproducible data, code, workflows, and/or tools) or willingness to contribute to open science.

Experience in integrating and analyzing large, and/or heterogeneous datasets.

Experience in working with a high performance computing or cloud computing environment is a plus (Earth Lab supports both HPC & cloud compute on AWS).

Demonstrated publication and grant-writing skills.

Team spirit and interest in interdisciplinary settings, with a willingness to engage with Earth Lab’s Analytics Hub and Education Initiative teams.


The University of Colorado offers excellent benefits, including medical, dental, retirement, paid time off, tuition benefit and ECO Pass. The University of Colorado Boulder is one of the largest employers in Boulder County and offers an inspiring higher education environment. Learn more about the University of Colorado Boulder.

Application Materials

To apply, please submit the following materials:

Resume or CV

Cover letter addressed to the Search Committee briefly describing your qualifications, professional goals, and specific interest in this position.

List of contact information for 3 references who will be willing to write a confidential Letter of Recommendation for you. 

If you are selected as the finalist, your degree will be verified by the CU Boulder Campus Human Resources department using an approved online vendor. If your degree was obtained outside of the United States, please submit a translated version as an optional attachment.


Review of applications will begin October 18. The position will remain open until filled.


Note: Application materials will not be accepted via email. For consideration, applications must be submitted through CU Boulder Jobs.

Job Number


Date Posted

Wednesday, September 25, 2019


Post-Doc Opportunity