Research Engineer

Digital Harbor Foundation•Baltimore, MD

1d•Remote

About The Position

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.

Requirements

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)

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

Benefits

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
401k Retirement Plan (with 6% matching)
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)