Research Scientist at Digital Harbor Foundation

Job description

Overview Current projections of sea level rise aren’t precise enough and potential solutions to mitigate the contributions of land based glaciers to sea level rise are underexplored. We aim to fund and accelerate existing research by increasing investments in Antarctic science as well as by internally developing accurate forecasts of sea level rise and the expected behavior of Antarctic glaciers. To achieve this we leverage state of the art computational methods combining physics and AI on remote sensing and field data. This role specifically is for the internal development of data driven physics and machine learning methods. Climate change is destabilizing marine ice sheets, creating a risk of catastrophic sea-level rise over the next century. Arête Glacier Initiative is a new nonprofit initiative dedicated to understanding this risk and assessing the efficacy, safety, and feasibility of potential interventions to stabilize ice sheets. We aim to fund, coordinate, and accelerate research to enable informed and legitimate societal decisions regarding these interventions. Minimum Qualifications • PhD in atmospheric science, geophysics, applied mathematics, computer science, or a closely related field • 3+ years of postdoctoral or equivalent research experience applying machine learning or deep learning to weather forecasting or Earth system modeling (e.g., forecasting, downscaling, emulation) • Proficiency in Python and relevant ML frameworks (PyTorch or JAX) with experience in high-performance or cloud computing environments and large-scale parallel processing • Experience handling noisy data, uncertainty propagation, and error estimation in a geoscientific context • Experience with remote sensing datasets, specifically satellite radar, altimetry, and optical imagery • Demonstrated ability to work effectively in cross-functional, multi-disciplinary teams Preferred Qualifications • Experience in running ice dynamics models such as ISSM, PISM or MALI • Experience with physics-informed neural networks, neural operators, Graph Neural Networks, or AI-accelerated FEM modeling • Familiarity with uncertainty quantification methods (e.g., ensembles, Bayesian inference) and sensitivity analysis techniques (e.g., adjoint methods) in a geoscientific context • Experience with large gridded dataset tooling (Zarr, xarray, Dask) Knowledge, Skills, and Abilities • Develop, optimize, and maintain scalable processing workflows and pipelines for Finite Element Modeling • Design and implement physics-informed machine learning models to improve predictive accuracy • Quickly learn and apply new tools, datasets, and methods to address evolving project needs • Apply advanced statistical methods to quantify uncertainty and validate model outputs • Communicate complex technical concepts clearly in both written and spoken English, with the ability to communicate effectively with diverse audiences • Demonstrated ability to work independently and collaboratively in a remote, fast-paced environment Additional Notes This position is fully remote with occasional in-person meetings as needed to fulfill the responsibilities of the position. Role and Responsibilities • Collaborate with data scientists, engineers and scientists to develop methods to forecast glacier ice flow and its impact on sea level rise. • Integrate domain knowledge into model design and interpret model outputs for physically realistic conditions • Develop, adapt, and evaluate probabilistic ML and hybrid physics-ML models for cryospheric processes including ice sheet flow, surface mass balance, and ocean-ice interactions • Develop and run large scale numerical simulations of ice dynamics. • Process and curate large-scale observational and synthetic datasets (e.g., satellite data, radar sounding and ice sheet model outputs) for use in model training and validation. • Identify sensitivities of model outputs to model imperfections of low quality source data or data gaps • Participate in regular team research reviews, contributing to and receiving feedback on methods and results Other Duties Please note that this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice. Arête Glacier Initiative is a newly launched nonprofit initiative, and as such, we are in the early stages of building our programs and team. This role requires a high degree of flexibility, adaptability, and a willingness to take on diverse tasks as we develop and grow. We encourage anyone who is interested in this role to apply, regardless of whether you feel you meet 100% of the qualifications. The top candidates will bring their own unique perspectives, experiences, and backgrounds from a variety of industries along with many but not necessarily all of the skills listed above. Compensation Compensation for this full-time position is $120,000 - $150,000 annually, commensurate with experience. Arête Glacier Initiative provides a best-in-class comprehensive set of benefits to support the team. All regular, full-time employees are eligible to receive: Health Benefits & Insurance • Carefirst Blue Cross Blue Shield - Health, Dental, and Vision Insurance (100% of the premium paid for employees and 85% of dependents) • Pre-Tax Health Savings Account (HSA) (with $275 monthly employer contributions) • Pre-Tax Flexible Savings Account (FSA) • Paid Accidental Death & Dismemberment (AD&D) Insurance • Paid Short-Term & Long-Term Disability Insurance • Paid Basic Life Insurance • Supplemental Voluntary Life Insurance (Employee, Spouse & Dependent Children) • Total Pet Plan and Supplemental Wishbone Pet Insurance • Employee Opportunity Program (EAP) - Health and Wellness • Wellness Reimbursement Program Retirement • 401k Retirement Plan (with 6% matching) Paid Time Off • 15 Days Paid Time Off Per Year • 16 Paid Holidays (14 common plus 2 flexible holidays, including Dec 25 - Jan 1) • Paid Bereavement Leave • Paid Parental Leave for Moms and Dads (two weeks after first year) If our mission and vision align with your personal values, please apply! A cover letter outlining your qualifications for the position along with your resume is required. Interviews will be conducted virtually. Arête Glacier Initiative is fiscally sponsored by Digital Harbor Foundation. Digital Harbor Foundation is dedicated to fostering learning, creativity, productivity, and community through education with a vision of digital equity for everyone. Driven by our sincere belief that access to opportunity is a basic right, we take bold yet practical actions to support making a better future now. Through a portfolio of projects focused on developing leadership within communities, we support those closest to challenges to take deliberate actions based in design thinking approach, backed by data analysis, grounded in a practice of collective impact, and driven by a commitment to racial equity. Digital Harbor and Arête Glacier Initiative are an equal opportunity employer.

Requirements

Must-haves

PhD in atmospheric science, geophysics, applied mathematics, computer science, or a closely related field
3+ years of postdoctoral or equivalent research experience applying machine learning or deep learning to weather forecasting or Earth system modeling (e.g., forecasting, downscaling, emulation)
Proficiency in Python and relevant ML frameworks (PyTorch or JAX) with experience in high-performance or cloud computing environments and large-scale parallel processing
Experience handling noisy data, uncertainty propagation, and error estimation in a geoscientific context
Experience with remote sensing datasets, specifically satellite radar, altimetry, and optical imagery
Demonstrated ability to work effectively in cross-functional, multi-disciplinary teams

Nice-to-haves

Experience in running ice dynamics models such as ISSM, PISM or MALI
Experience with physics-informed neural networks, neural operators, Graph Neural Networks, or AI-accelerated FEM modeling
Familiarity with uncertainty quantification methods (e.g., ensembles, Bayesian inference) and sensitivity analysis techniques (e.g., adjoint methods) in a geoscientific context
Experience with large gridded dataset tooling (Zarr, xarray, Dask)
Knowledge, Skills, and Abilities
Develop, optimize, and maintain scalable processing workflows and pipelines for Finite Element Modeling
Design and implement physics-informed machine learning models to improve predictive accuracy
Quickly learn and apply new tools, datasets, and methods to address evolving project needs
Apply advanced statistical methods to quantify uncertainty and validate model outputs
Communicate complex technical concepts clearly in both written and spoken English, with the ability to communicate effectively with diverse audiences
Demonstrated ability to work independently and collaboratively in a remote, fast-paced environment
Additional Notes

Application prep

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Keywords to mirror

aimachine learningpythonml

Platform: jazzhr
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Paid Time Off

Parental Leave

Wellness