Postdoctoral Scholar - AI in Earth and Environmental Sciences

Postdoctoral Scholar – AI in Earth and Environmental Sciences

Location: Syracuse, NY

Pay Range: $62,400 - $70,000

FLSA Status: Exempt

Hours: Determined by supervisor.

Job Type: Full Time

Rank: Post Doctoral

Union Representation: SEIU, Local 200

The Department of Earth and Environmental Sciences at Syracuse University invites applications for a Postdoctoral Scholar position in the Hydrogeochemistry and Environmental Data Sciences (HANDS) research group. The position is broadly focused on artificial intelligence, machine learning, environmental data science, foundation AI models, and data‑intensive Earth and environmental research.

The successful candidate will contribute to two complementary research directions. First, AI/ML, data science, and geologic/environmental datasets will be used to assess energy and environmental systems, including oil and gas well condition, characterization, and integrity‑related questions. Second, the research will focus on characterizing global water and elemental cycles, with emphasis on terrestrial and catchment systems. Together, these projects use large geochemical, hydrologic, geospatial, regulatory, and environmental datasets to advance predictive, interpretable, and transferable approaches for Earth and environmental sciences.

A key intellectual theme is the development and application of AI/ML and foundation‑model approaches for complex Earth and environmental systems, including subsurface energy infrastructure, riverine hydrogeochemistry, watershed elemental cycles, water quality, terrestrial water and solute fluxes, and prediction across watershed and river‑network scales.

• Ph.D. in geoscience, hydrology, geochemistry, environmental science, civil/environmental engineering, data science, computational geoscience, Earth system science, or a closely related field by the anticipated start date.
• Demonstrated experience in artificial intelligence, machine learning, environmental data science, statistical modeling, or related quantitative methods.
• Strong quantitative, programming, and data analysis skills.
• Ability to work with complex environmental, geospatial, hydrologic, geochemical, or Earth system datasets.
• Ability to develop reproducible computational workflows.
• Evidence of scientific communication through publications, presentations, reports, software, datasets, or related scholarly products.
• Ability to work both independently and collaboratively in an interdisciplinary research environment.

• Experience or interest in AI/ML, statistical modeling, or data science applications in energy and environmental systems.
• Knowledge of oil and gas well datasets, well characterization, well integrity assessment, subsurface energy systems, environmental risk assessment, or related geologic/environmental infrastructure questions.
• Experience with foundation AI models, representation learning, transfer learning, self‑supervised learning, deep learning, interpretable machine learning, uncertainty quantification, data assimilation, or related AI/ML approaches for scientific datasets.
• Application of AI/ML or foundation‑model approaches to catchment sciences, hydrology, hydrogeochemistry, water quality, watershed elemental cycles, river networks, or Earth system prediction.
• Experience working with large environmental, geochemical, hydrologic, geospatial, regulatory, remote sensing, or Earth system datasets.
• Experience integrating diverse datasets such as stream chemistry, discharge, hydroclimatic forcings, land cover, lithology, soils, well records, regulatory data, remote sensing products, geospatial attributes, monitoring data, or modeled Earth system outputs.
• Experience developing predictive, interpretable, and transferable models for environmental, geologic, energy, or Earth system applications.
• Experience with scientific programming in Python, R, or similar languages.
• Experience using reproducible research tools such as Git/Git Hub, Jupyter notebooks, R Markdown/Quarto, workflow managers, open‑science repositories, cloud computing, or high‑performance computing resources.
• Research experience or strong interest in hydrology, geochemistry,…