[ES_JOBS_NET] Rsch Fac in Forest Remote Sensing w/ GEDI & EO: University of Maryland, College Park

[Tali Schwelling]

Good morning,

The Department of Geographical Sciences at the University of Maryland,
College Park, is currently looking to fill several Professional Track
Research Faculty positions. These non-tenure opportunities are open at the
levels of Postdoctoral Associate or Assistant Research Professor, based on
the successful candidate's qualifications and experience. We offer highly
competitive salaries and benefits packages. The roles encompass a broad
spectrum of activities including but not limited to supporting projects
linked to the *Global Ecosystem Dynamics Investigation (GEDI)* [
https://gedi.umd.edu], NASA’s Carbon Monitoring System (CMS) [
https://carbon.nasa.gov/cms/], and development of methods for mapping and
monitoring mature and old-growth forests.

The GEDI mission focuses on biomass estimation, biodiversity, habitat
characterization, forest complexity, and prognostic ecosystem models, and
is slated to resume operations in late 2024 for a minimum of three years.
An important aspect of our current initiatives focuses on the integration
of GEDI data with other Earth Observation (EO) data such as from passive
optical/stereo and Synthetic Aperture Radar (SAR) technologies. The latter
includes data from TanDEM-X, ALOS-2 and Sentinel-1, as well as the
forthcoming NISAR and BIOMASS missions. Successful candidates will
participate in diverse aspects of GEDI-related science analyses and
projects. This participation includes refining and validating science
algorithms, post-flight calibration and validation, developing field
observation databases, science data product development and the fusion of
multi-sensor data. There is also the opportunity to utilize these remote
sensing data in science investigations within the candidate's areas of
interest.

Our NASA CMS projects are focused on combining GEDI and interferometric SAR
(InSAR) data to map high-resolution biomass and its changes, in
collaboration with partner institutions including the German Aerospace
Center (DLR), alongside activities utilizing these data to drive ecosystem
and diversity models. Our mature and old-growth forest work is in
partnership with the U.S. Forest Service, NASA Goddard Space Flight Center
(GSFC), and Harvard Forest. This research is developing methodologies for
the assessment and monitoring of mature and old-growth forests using a
comprehensive range of EO data, modeling, and in situ national forest
inventory data. These projects have significant engagement with
stakeholders at the local, national, and international levels.

Ideal candidates will have a background in fields related to Earth
observation and terrestrial ecology, with demonstrated interests in remote
sensing science, machine learning, ecosystem structure and biomass,
ecosystem modeling, and studies on habitat/diversity, among others.
Technical expertise in lidar (terrestrial, airborne, or spaceborne) and/or
SAR remote sensing is highly desirable. Nonetheless, applicants with strong
backgrounds in other remote sensing domains or those skilled in applying
machine learning or statistical analyses to remote sensing data are also
welcome to apply.

The positions are co-located with the GEDI research group at the
University’s Discovery District [
https://innovate.umd.edu/resources/discoverydistrict], Maryland's largest
research park, located off-campus yet in close proximity. Additionally,
these positions may require domestic and international fieldwork to support
research objectives and product development.

Minimum Qualifications and Required Skills

Candidates must possess a doctoral degree in Geographical Sciences or a
related field within environmental science, such as Biology or Forestry.
Those with doctoral degrees in other disciplines (e.g., Physics, Computer
Science, Electrical Engineering) who demonstrate substantial knowledge and
understanding of land surface remote sensing are also eligible. Essential
skills include competency in programming and statistical analysis, with
experience in languages and tools such as Python, IDL, MATLAB, C/C++, R,
PyTorch, TensorFlow. For the Assistant Research Professor level, a proven
track record of independent research and peer-reviewed publications is
required.

Preferred Qualifications

Experience with lidar remote sensing using GEDI data and/or experience with
SAR remote sensing. Candidates should have experience and expertise in
working effectively with individuals from diverse backgrounds.

Application Process

Interested candidates should submit an application including a personal
statement detailing their background and experience relevant to the role, a
current, signed, and dated Curriculum Vitae, reprints or URLs for selected
peer-reviewed publications, and the contact details (including email
addresses) for 3-5 references. Candidates are encouraged to reach out to
Ralph Dubayah (dubayah at umd.edu) for discussions on potential research
interests they wish to pursue at the University of Maryland.

Applications must be submitted by March 30th, 2024, for priority
consideration, although the search will remain open until the positions are
filled.

How to Apply

Submit your application through the University of Maryland employment
portal: ejobs.umd.edu/postings/116918. We particularly encourage
applications from women and minorities. The University of Maryland is
committed to diversity and inclusivity, operating as an Equal Opportunity
Affirmative Action Employer.

Additional Information

For more details about the department's research programs, please visit
http://www.geog.umd.edu or contact us directly at the provided address.

Please contact me or Dr. Ralph Dubayah (dubayah at umd.edu) if you have any
questions!

Best,
Talia

-- 
Talia Schwelling (she/her)
Faculty Specialist
Department of Geographical Sciences
University of Maryland College Park
Office: 301-405-3144 | Cell: 443.414.8735 | Email: tschwell at umd.edu
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