COMPUTATIONAL SCIENTIST, YALE CENTER FOR NATURAL CARBON CAPTURE
The Yale Center for Natural Carbon Capture (YCNCC) at Yale University is seeking highly qualified candidates who are passionate about mitigating climate change to manage and direct the data and scientific computing needs across the YCNCC. The successful applicant will collaborate with an interdisciplinary team of climate scientists, physicists, chemists, oceanographers, geochemists, and ecologists to advance the research and deployment of climate change solutions developed at the YCNCC.
As an Affirmative Action/Equal Opportunity employer, Yale University values diversity among its students, staff, and faculty and strongly welcomes applications from women, persons with disabilities, protected veterans, LGBTQ community members, and persons from racial/ethnic groups historically underrepresented in earth and environmental sciences.
All applications should be submitted through Interfolio at http://apply.interfolio.com/136985. Review of applications will begin on January 22, 2024, and continue until a suitable candidate is identified.
Key responsibilities
● Support YCNCC researchers on their data and scientific computing needs
● Develop and implement a five-year strategy for YCNCC’s scientific computing effort
● Analyze large datasets from earth-system models, ocean models, satellite observations, remote sensing, and field experiments using statistical and machine learning tools
● Develop decision support tools for climate change solutions based on YCNCC research
● Work with YCNCC researchers on proposal development, submission, and execution
● Co-author research findings with YCNCC researchers in leading journals
Requirements
● PhD in computer science, data science, machine learning, artificial intelligence, physical oceanography, climate science, or related fields
● Extensive experience with data science and high-performance computing
● Excellent writing and oral communication skills
● Ability to, and experience with, working collaboratively in interdisciplinary teams
● Ability and willingness to quickly learn new skills
● Record of publishing high-impact journal papers
Preferred Qualifications
Note: We encourage applications from candidates who meet one or more of these qualifications. We do not expect that any single candidate will possess all listed qualifications.
● Experience with large earth-system datasets (ECCO, GLODAP, PRISM, SSURGO, etc.)
● Experience with biogeochemical modeling (DayCent, DNDC)
● Experience with geospatial analysis and environmental remote sensing datasets
● Programming experience in or ability to learn Fortran / C
● Background in ocean, climate, and/or earth-system modeling. Experience with common community models (POP, MITgcm, CESM, etc.)
● Knowledge of Physics-informed NN frameworks such as SimGAN or NVIDIA’s Modulus
● Proficiency in machine learning frameworks (TensorFlow, PyTorch, sklearn, etc.) and languages (Python, C)
● Experience applying deep learning and other AI methods to emulate complex model parameterizations
● Knowledge of deep learning and neural networks, generative adversarial networks, time series predictive methods such as LSTMs, Bayesian machine learning methods, and statistical learning
● Experience with or knowledge of operations research and optimization methods (e.g., convex optimization, nonlinear methods, integer programming, etc.) and associated tools such as Gurobi or Python packages like “or-tools”
● Experience with Bayesian emulation and hierarchical models
● Experience with uncertainty quantification and bootstrapping or Markov Chain Monte Carlo (MCMC) methods
● Experience with climate model emulators