Postdoctoral Researcher in Ice Sheet Modeling with Machine Learning
- Employer
- Dartmouth College
- Location
- Hanover, New Hampshire, United States
- Salary
- Competitive Salary
- Date posted
- Nov 13, 2024
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- Position Type
- Faculty Positions, Science & Mathematics, Computer Sciences & Technology, Geology & Earth Sciences, Social & Behavioral Sciences, Other Social & Behavioral Sciences
- Employment Type
- Full Time
Job Details
The Ice Future Lab in the Department of Earth Sciences at Dartmouth invites applications for a postdoctoral researcher with expertise in ice sheet numerical modeling and machine learning. This is a 2-year full-time position in residence at Dartmouth in Hanover, NH, with start date preferably in June 2025.
The project, focusing on the dynamics of tidewater glaciers in Greenland, aims to develop a more realistic parameterization of calving law by training machine learning models on reanalysis and observational data, thereby advancing our capacity to predict future glacier behavior under climate change. The postdoctoral researcher will work using both traditional numerical method and physics-informed machine learning to improve our understanding of the calving dynamics of tidewater glaciers, with a focus on Helheim Glacier, Southeast Greenland. This position is funded by NASA in collaboration with the NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions.
Dartmouth is a research-intensive Ivy League university with graduate programs in the sciences, engineering, medicine, and business. Postdoctoral scholars are supported by the Guarini School for Graduate and Advanced Studies , including their diversity and inclusion initiatives . Dartmouth is committed to academic excellence and encourages the open exchange of ideas within a culture of mutual respect. People with different backgrounds, life experiences, and perspectives make the Dartmouth community diverse, which enhances academic excellence. In addition, we value applicants who have a demonstrated ability to contribute to Dartmouth’s diversity initiatives in STEM research, such as the Women in Science Project , the EE Just STEM Scholars Program , and the Academic Summer Undergraduate Research Experience . Applicants should address in their cover letter how their research, teaching, service, and/or life experiences prepare them to advance Dartmouth's commitment to diversity in service of academic excellence.
Qualifications
- A Ph.D. in Earth Sciences, Glaciology, Climate Science, Applied Mathematics, Computer Science, or a related field at the time of starting the position.
- Strong experience in numerical modeling of ice dynamics and/or machine learning methods, particularly with neural networks.
- Proficiency with ice sheet modeling software, and experience working with large geospatial datasets are preferred.
- Familiarity with remote sensing datasets relevant to glacier and ocean studies, as well as skills in data assimilation and model calibration.
- Programming experience in Python, MATLAB, or other relevant languages.
- Strong track record of relevant publications in peer reviewed journals.
- Strong oral and written communication skills.
The successful candidate will play a key role in developing a numerical model of Helheim Glacier using the Ice-sheet and Sea-level System Model (ISSM) and employing transient calibration techniques. This reanalysis will serve as a robust dataset, enabling the use of Deep Learning methods—specifically, a Deep Operator Network (DeepONet)—to infer calving front migration based on a combination of ice dynamics and climate forcings.
Key responsibilities include:
- Developing a numerical model to simulate Helheim Glacier over the past 40 years.
- Applying machine learning methodologies to infer relationships between climate forcing, ocean conditions, and glacier dynamics.
- Integrating diverse remote sensing datasets for training the machine learning model.
- Collaborating with project partners, including NASA and NSF research teams.
- Presenting research findings at national and international conferences and publishing results in peer-reviewed journals.
Please submit the following materials electronically via Interfolio.
- Cover letter, including contact information for three references.
- CV.
Review of applications will begin on February 1, 2025 ; applications submitted after this date will be reviewed until the position is filled. Recommendations letters will be requested for finalists only. For questions about the position, please contact Dr. Gong Cheng, ( g ong.cheng@dartmouth.edu ) with “Helheim ice modeling postdoc” in the subject line.
Equal Employment Opportunity Statement
Dartmouth College is an equal opportunity/affirmative action employer with a strong commitment to diversity and inclusion. We prohibit discrimination on the basis of sex, race, color, religion, age, disability, status as a veteran, national or ethnic origin, sexual orientation, gender identity, gender expression, or any other category protected by applicable law, in the administration of its educational policies, admission policies, scholarship and loan programs, employment, or other school administered programs. Applications by members of all underrepresented groups are encouraged.
If you are an applicant with a disability and need accommodations to assist in the job application or interview process, please email ADA@dartmouth.edu . In the subject line, please state “Application Accommodations” and include the job number or title. Someone from the ADA Compliance Office will be in touch within 2 business days.
For additional employment opportunities at Dartmouth College, please visit the Dartmouth Interfolio Job Board , the Office of the Provost , and the Office of Human Resources .
Offers of employment are contingent upon consent to a pre-employment background check with results acceptable under Dartmouth policy. Please visit the Office of Human Resources for details.
All Dartmouth College employees must comply with the College’s health and safety guidelines and protocols, including but not limited to those related to COVID-19, such as any testing, masking, or distancing requirements that may be in place at any given time or place.
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