Hiring


Professional, postdoctoral, and student opportunities 

Interested in becoming part of the AI2ES Center? Open positions funded by the Center are listed here.

Contact links with details are provided in each listing.  Apply to each position directly, not through this site.

Colorado State University

Postdoctoral Fellow Data Assimilation and Machine Learning Scientist

Location: Boulder, CO, USA

Description: The Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University (CSU), located on the CSU Foothills Campus approximately 5 miles northwest of CSU main campus, seeks to fill a postdoctoral fellowship in April/May 2022 as part of a National Science Foundation (NSF) award to train a new scientist in data assimilation and machine learning techniques. This fellowship is intended for persons who have recently completed their Ph.D and may last up to 18 months contingent upon NSF funding availability. The individual in this position will serve as a member of the CIRA data assimilation group and will test the robustness of machine learning techniques to identify the links between non-Gaussian distributions and different atmospheric scale dynamics, convert the hybrid version of WRF-GSI(JEDI) to have a non-Gaussian component, and assess the robustness of new non-Gaussian based ensemble systems along with advancing the development of a new version of the Maximum Likelihood Ensemble Smoother.

NOTE: In your cover letter, please specifically address the required and preferred qualifications of this position. A cover letter that fails to address the required and preferred qualifications of this position may not be further considered after review by the search committee.

CSU is an EO/EA/AA employer and conducts background checks on all final candidates.

Full consideration deadline: March 13, 2022.

Link to position details and application

Texas A&M University – Corpus Christi

Supervisors: Dr. Scott King and Dr. Philippe Tissot

Ph.D. Graduate Student

Location: Corpus Christi, TX, USA

Description: Texas A&M University-Corpus Christi (TAMU-CC) seeks one Ph.D. graduate researcher with a prior degree in computer science, geospatial science, with some background in the environmental sciences. Additional experience in machine learning, physical science, or environmental science preferred. A master’s degree is preferred, however strong candidates with a BS will be considered. The student will join the research team of the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES.org) at the TAMU-CC Conrad Blucher Institute. The team has developed a deep learning model to predict coastal fog, FogNet (https://doi.org/10.1016/j.mlwa.2021.100038). The present model and its architecture were developed for a single location. The new student will investigate implementation of the model to other locations and different environmental settings while developing a broader set of predictors to include numerical weather predictions, satellite imagery and coastal measurements while gaining physical insights into the related coastal process using explainable AI (XAI). The candidate will work in close collaboration with other TAMU-CC PhD students and AI2ES partners at other universities, the local national weather service and the private sector. The student will need to be accepted to the TAMU-CC Geospatial Computer Science program or alternatively the TAMU-CC Coastal and Marine System Science program. The position comes with a monthly stipend, tuition and fees. Preferred start date is spring or summer 2022. To express interest or apply send your CV with an email sharing why you are interested in the position to Drs Scott King (scott.king@tamucc.edu) and Philippe Tissot (philippe.tissot@tamucc.edu).

 

Contact Professor Tissot to learn more

The University at Albany (UA) Atmospheric Sciences Research Center (ASRC)

Mentors: Dr. Christopher Thorncroft and Dr. Kara Sulia

Ph.D. Graduate Student

Location: Albany, NY, USA

The University at Albany (UA) Atmospheric Sciences Research Center (ASRC) in Albany, NY seeks two Ph.D. graduate research associates with background in machine learning and/or the physical sciences (preferably atmospheric science).

TWO AREAS OF RESEARCH
1. Regional Sensitivity to Winter Weather. The student will perform NY state holistic winter weather analysis, with focus on variations and sensitivities among climate regions and their influence on predictability. The student will be responsible for developing machine-learned models and employing other statistical techniques to identify patterns and pattern variability in winter weather events and impacts across the state. Forecast, reanalysis, and ensemble products (e.g., GFS, GEFS, NAM, HRRR) along with data from the NYS Mesonet, will serve as inputs, with the goal of assessing regional winter-weather predictability hours to days.
2. The Impact of Winter Weather on Roadways. The student will investigate the predictability of winter-weather effects on NY state roadways. The student will be responsible for developing machine-learned models and employing statistical techniques to identify patterns in meteorological (e.g., NYS Mesonet) and non-meteorological (e.g., traffic flow) datasets. The student will also be responsible for the visualization of results actionable to the end-user. Collaboration with NY State transportation sectors are expected, as well as emergency managers and decision makers.

Contact Dr. Kara Sulia and/or Dr. Christopher Thorncroft

Research Experiences for Undergraduates (REU)

Undergraduate Students

Location: Norman, OK, USA; Virtual participation an option

The summer research program pairs undergraduate students with research mentors to conduct a project in the wide-ranging meteorology, climate, radar engineering, machine learning, computer science, and interdisciplinary topics. Mentors come from the AI2ES team.

Potential projects include:

  • Using Machine Learning to improve thunderstorm prediction from the NOAA Warn-on-Forecast System
  • Estimating convective updraft characteristics from radar
  • Hail forecasting and analysis
  • More projects coming soon!

Eligibility:

  • Pursuing an undergraduate degree
  • Graduating no sooner than December after the summer they participate*
  • A U.S. Citizen or Permanent Resident of the U.S.

*Students who are graduating from a 2-year degree program are eligible to participate so long as they are enrolled in and will start the remaining portion of a 4-year degree in the fall after their summer participation.

Additional REU locations may be added for 2022.

visit caps.ou.edu/reu/application.html to apply

The Future of Climate at IBM Research is looking to hire summer interns for 2022!

We have two types of internship positions – research and software engineering – for which applicants can follow the links below. Please note that the job descriptions in each link are generic for applications to all of IBM Research. Our teams are searching for candidates with strong experience in one or more of geospatial data analysis, AI/ML, remote sensing, and software engineering.
AI2ES candidates should reach out to Campbell Watson directly when applying to ensure their online applications are considered for the Future of Climate theme. Click on the links for more information:

Research Intern

Software Engineering Intern

We work on a range of research problems – from building technology to predict and adapt to the perils of climate change to accelerating the discovery of new materials for carbon capture and storage. Here are some recent blogs about the research we’re undertaking:

Central Michigan University

Mentor: Dr. John Allen

Ph.D. Graduate Student

Location: Mt Pleasant, MI, USA

The Climate and Severe Weather Research Group at Central Michigan University (CMU) is recruiting a Ph.D. student to join our group in Summer 2022. The student will contribute as part of a team looking at historical and future risk of thunderstorm hazards. The project will leverage a combination of observations, and large reanalysis and climate model datasets to explore topics such as the local rate of return of severe convective events such as hail or wind. Techniques involve will include machine learning and extreme value modeling, with application to risk assessment. This project is grant supported, and offers a competitive stipend, with four years of research assistantship available. Preferred qualifications are an M.S. in meteorology, atmospheric sciences, environmental data science or climate science, but exceptional bachelors level applicants would also be considered. Statistical, Machine Learning and programming experience would also be desirable skills. If you would like more information please email allen4jt@cmich.edu, and to be considered send a CV and letter of interest. The successful applicant will be invited to apply to the Earth and Ecosystem Ph.D. program at Central Michigan University. Details about our group and research at CMU can be found here.

for more information, email Dr. John Allen

PARC, a Xerox company

Postdoctoral Position in Development of AI/ML models for Large-scale climate effects

We are looking for a postdoctoral researcher with expertise in applying AI models for climate to join Palo Alto Research Center to work on a project funded by DARPA AI-Assisted Climate Tipping point Modeling (ACTM).

We are seeking an energetic, enthusiastic individual for a postdoctoral research position on using state-of-the-art Artificial Intelligence methods for optimizing global-scale climate interventions. The postdoctoral fellow will work collaboratively with climate dynamics researchers at the University of Victoria to create a climate response function that maps the regional (shortwave) cloud radiative effect to the large-scale coupled (atmosphere and ocean) circulation and regional climate.

We expect the successful candidate to have the following background and expertise:

  • Knowledge and experience in applying state-of-the-art AI methods for climate/weather data
  • Experience with processing Earth System models (CESM, E3SM);
  • Experience with handling, processing, and analyzing large climate model output datasets (from CMIP5 or CMIP6);
  • Experience with scientific computing and data analysis using Python;
  • Experience working as a collegial team member in an interdisciplinary group of scientists; and
  • Commitment to meeting project deadlines.

Minimum qualifications for this postdoctoral position include the following:

  • A PhD in Computer science, Atmospheric Sciences, Physical Oceanography, Applied Mathematics, or a related field.
  • Experience in developing machine learning and deep learning architectures, particulatly for climate and weather applications
  • Experience with scientific computing and high-performance computing on parallel architectures.
  • Experience in common deep learning frameworks (Tensorflow, Pytorch)

PARC, a Xerox company, is in the Business of Breakthroughs. Practicing open innovation, we provide custom R&D services, technology, expertise, best practices, and intellectual property to Fortune 500 and Global 1000 companies, startups, and government agencies and partners. We create new business options, accelerate time to market, augment internal capabilities, and reduce risk for our clients. Since its inception, PARC has pioneered many technology platforms – from the Ethernet and laser printing to the GUI and ubiquitous computing – and has enabled the creation of many industries. Incorporated as an independent, wholly owned subsidiary of Xerox in 2002, PARC today continues the research that enables breakthroughs for our clients’ businesses.

Xerox is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, creed, religion, ancestry, national origin, age, gender identity or expression, sex, marital status, sexual orientation, physical or mental disability, use of a guide dog or service animal, military/veteran status, citizenship status, basis of genetic information, or any other group protected by law. Learn more at www.xerox.com and explore our commitment to diversity and inclusion! People with disabilities who need a reasonable accommodation to apply or compete for employment with Xerox may request such accommodation(s) by sending an e-mail to XeroxStaffingAdminCenter@xerox.com. Be sure to include your name, the job you are interested in, and the accommodation you are seeking. 2020 Xerox Corporation. All rights reserved. Xerox and Xerox and Design are trademarks of Xerox Corporation in the United States and/or other countries.

Disaster Tech

Research Assistant

Location: remote

Position Summary

Disaster Tech has developed an emergency management platform, the Data science Integrated Collaboration Environment (DICE), which pulls in a wide range of data that is analyzed and visualized. This gives emergency managers a comprehensive overview of an incident for better situational awareness and easy communication. Interns will work closely with members of the science and engineering team to conduct independent research on projects that support the science and tee hnology road maps. Applicants from a broad range of academic backgrounds (e.g., earth sciences, computer science, decision science) are encouraged to apply. Preference is given to candidates enrolled in a 4-year undergraduate degree program entering their junior or senior year. Graduate students are also encouraged to apply. This position is a 10-week paid internship of 10 hours a week starting in June. Pay is between $15 – $20 an hour based on skills and experience. This internship is fully remote.

Potential Research Project Areas

  • Fire weather prediction and risk assessment
  • Power outage prediction and validation
  • Intelligent transportation systems evaluation
  • Social science – evaluating human decision making and user experience on DICE
  • Applying machine learning to evaluate risk and optimize planning or response decisions

Preferred Skills and Experience

  • Aptitude and attitude to learn about the nexus of emergency management, science, and technology
  • Basic understanding of the use of open data and open-source software
  • Work independently with minimal supervision
  • Have effective and clear communication skills
  • Has some data analysis experience using one or more software packages
  • GIS knowledge of processing and managing vector and raster data
  • Some understanding or awareness of machine learning capabilities and application

Company Overview

Disaster Tech is a practitioner led public benefit company that provides risk managers with innovative tools to prepare for disasters, build resilient communities, and mitigate risk. Through industry experts, a world-class science and engineering team, and partnerships with academia, public, and private sectors, Disaster Tech is disrupting the way in which emergency management has been done by creating new and innovative decision science technologies.

Disaster Tech is an equal opportunity employer. Al I qualified applicants wi II receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, veteran status, and other classifications covered under federal, state, or local laws. Auxiliary aids and services are available upon request to individuals with disabilities.

 

To apply: Send a cover letter and resume to Jay Shafer at jason@disastertech.com by Tuesday, April 26.

 

 

Radiant Earth Foundation

Senior Geospatial Data Scientist

We are searching for a passionate and experienced Senior Geospatial Data Scientist to build novel machine learning models for various applications using Earth observations and lead our ML projects portfolio including but not limited to generating new training datasets, organizing competitions, and developing training and capacity development programs.

Our team is currently distributed across the US and Nigeria and you will be working remotely within the US.

What You Will Do:

  • Build machine learning models for various applications in agriculture, land monitoring, and infrastructure mapping.
  • Lead the design of competitions based on Radiant MLHub datasets.
  • Prepare tutorials and training materials for machine learning applications using datasets and models on Radiant MLHub.
  • Work closely with Engineering Lead and Technical Project Manager to lead machine learning projects.
  • Develop and propose new project ideas for the Company to pursue.

Basic Qualifications:

  • +3 yrs of experience in geospatial machine learning projects.
  • Bachelor’s or Master’s degree in Computer Science,
  • Environmental Science, Remote Sensing or relevant field.
  • Experienced in Python and Jupyter Notebooks.
  • Experienced in SQL.
  • Deep knowledge of and experience in using machine learning techniques.
  • General knowledge of geospatial and remote sensing data.
  • Experienced working with machine learning libraries such as PyTorch or TensorFlow.
  • Working knowledge of geospatial packages (GDAL, Shapely, GeoPandas, rasterio).
  • Experience in data visualization in Python.
  • Experience with version-control platforms (e.g. GitHub, GitLab).
  • Excellent communication and team collaboration skills.
  • You are passionate about the world’s challenging problems, and eager to help address them.
  • Eligible to work in the US (visa sponsorship is not provided).

Preferred Qualifications:

  • Experience leading machine learning projects.
  • Familiarity with SpatioTemporal Asset Catalog (STAC).
  • Experience working in a commercial cloud environment (Azure or AWS).

What we offer:

Radiant Earth offers a competitive salary and attractive benefits package with flexible time off. We work with a diverse user community, and you will have the opportunity to work on highly impactful projects. We have a collaborative work environment with a robust support system driven by passionate individuals.

While we sincerely appreciate all applications, only those candidates selected for an interview will be contacted. All applications are considered confidential. Radiant Earth Foundation is an equal opportunity employer.

for more information visit the position announcement

Radiant Earth Foundation

Geospatial Software Engineer

We are searching for a Geospatial Software Engineer to help in the buildout and maintenance of Radiant MLHub API and Python Client. You will work closely with other members of the engineering team.

Our team is currently distributed across the US and Nigeria and you will be working remotely within the US.

What You Will Do:

  • Maintain and expand Radiant MLHub API services.
  • Maintain and expand Radiant MLHub Python client.
  • Support development of STAC ML-Model Extension.
  • Prepare tutorials and documentation for new API services and Python Client releases.

Basic Qualifications:

  • Experienced in Python and Jupyter Notebooks
  • Experienced in PostgreSQL and PostGIS (or other relational database and GIS software).
  • Have Linux command-line and general shell scripting experience.
  • Have some Docker experience.
  • Working knowledge of version-control platforms (e.g. GitHub, GitLab).
  • Basic knowledge of geospatial and remote sensing techniques.
  • Excellent communication and team collaboration skills.
  • You are passionate about the world’s challenging problems, and eager to help address them.
  • Eligible to work in the US (visa sponsorship is not provided).

Preferred Qualifications:

  • Expert in Python.
  • Working knowledge of geospatial Python packages (GDAL, Shapely, GeoPandas, rasterio).
  • Familiarity and experience with SpatioTemporal Asset Catalog (STAC).
  • Familiarity with FastAPI and/or PgSTAC.
  • Have designed and implemented REST APIs.
  • Have significant Docker experience.
  • Experienced in deploying and using cloud infrastructure (Azure is preferred).
  • General knowledge of geospatial and remote sensing techniques.
  • Familiarity with machine learning techniques.

What we offer:

Radiant Earth offers a competitive salary and attractive benefits package with flexible time off. We work with a diverse user community, and you will have the opportunity to work on highly impactful projects. We have a collaborative work environment with a robust support system driven by passionate individuals.

While we sincerely appreciate all applications, only those candidates selected for an interview will be contacted. All applications are considered confidential. Radiant Earth Foundation is an equal opportunity employer.

for more information visit the position announcement