Please note: This posting has been updated in its Position Highlights, Description and Preferred Qualification sections and in its number of vacancies from 1 to 2. We seek a post-doctoral researcher to map floods using machine learning and satellite imagery to understand inequity in urban flood adaptation and build capacity for environmental justice organizations. This person will join the Social Pixel lab to improve on existing models with new methods to produce a historic US flood database and enable end-users to produce flood maps for their own communities. This position is funded through an NSF Career Award and NASA grants. The NSF project "Addressing flood justice and equity impacts of adaptation and urban expansion with satellite observations" will assess to what extent flood mitigation infrastructure is responsible for increasing inequitable flood exposure. The post doc will lead building 2 databases i) US Floodplain Mitigation Infrastructure Database (from FEMA GIS data) and ii) the US Flood Extents in Revised Floodplains Database (using satellite data including Planetscope data and public sensors). The pipelines used to make flood maps will support curriculum to build capacity with staff from environmental justice organizations recruited as paid fellows. NASA funded projects include operationalizing VIIRS for water mapping, river width evaluation for SWOT reaches, and flood mapping in Bangladesh. The postdoc will work help NASA and end users deploy machine learning models of high accuracy, and work with existing research assistants and post docs to improve deep learning models of inundation. We will improve on our existing CNN (Convolutional Neural Network) models across 6 sensors using new techniques such as transformers. We have large hand labeled datasets across sensors for flooding and the post doc will have an opportunity to quickly run experiments and write papers with our datasets. The Social Pixel lab has a strong emphasis on applications for social and environmental justice, and you will have the opportunity to work with actors outside academia in government, business, and non-profits we partner with.

There is some flexibility in the FTE for this position, which will be discussed at time of interview.

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Job Duties:

• Collaborate with PI, and students to deploy deep learning models to identify and monitor water in satellite imagery time series using sensors such as MODIS, VIIRS, Sentinel-1, and Planetscope.
• Build a database of flood mitigation infrastructure in the US.
• Build a history of flooding in the US at spatial high resolution.
• Writing articles for publication (co-authored and first-authored).
• Support organization of meetings, collaboration, and lead NASA funded projects related to flood mapping in Bangladesh and near real time flood mapping using VIIRS.
• Present results at AGU, AAG, or other relevant conferences.
• Support grant proposal writing efforts and developing proof of concepts (including opportunities to lead ones' own).
• Collaborate with partners at NASA to implement flood water detection models in satellite imagery.

Additional Information:

• Researcher will gain skills in fundraising (grant writing), mentoring, and research administration essential for future faculty, government, and industry research positions, with hands on mentoring from the PI to help you achieve your career goals. The researcher will also be given at least 20% of their time to devote to personal projects, publications, and funding proposals. The University of Arizona is a Hispanic-Serving Institution, and the Social Pixel lab is committed to diversity, inclusion, and social justice inside the lab and in our research practices- see more- https://beth-tellman.github.io/Empowerment%20Vision%20and%20Operation%20(1).pdf

• PhD in Geography, Environmental Sciences, Computer Science, or related field upon hire.
• Experience in Remote Sensing and GIS.
• Experience in Machine Learning preferably using Python.
• At least one first authored publication in a referred journal.

• PhD in Geography or Environmental Science.
• Experience programming in Google Earth Engine.
• Experience with data fusion (radar, optical imagery, of various spatial and temporal resolutions).
• Experience working on urban adaptation.
• Experience working with end users, especially environmental justice organizations or communities.
• Experience working with hydrological data (such as stage/discharge measurements, surface water measurements).
• Experience in training deep learning models, such as convolutional neural networks, using Pytorch or Fastai.
• Experience in data engineering- curating and maintaining labeled training datasets for deep learning.
• Experience with transformers, LSTMs, and other deep learning modalities.