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 Job Description 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. Qualifications 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. Preferred Qualifications 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/GitHub, 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, terrestrial water and elemental cycles, energy/environmental systems, catchment sciences, or Earth system science. Experience mentoring students or collaborating in interdisciplinary research teams. Strong written and oral communication skills. Responsibilities Develop and apply artificial intelligence, machine learning, statistical modeling, foundation‑model, and environmental data science approaches to large geochemical, hydrologic, geospatial, regulatory, and related Earth system datasets. Develop AI/ML‑enabled workflows to characterize energy and environmental systems, including oil and gas well condition and integrity‑related questions, using geologic, environmental, and publicly available or regulatory datasets. Investigate the potential application of foundation AI models in catchment sciences, including riverine hydrogeochemistry, watershed elemental cycles, water quality, terrestrial water and solute fluxes, and environmental prediction across catchment, watershed, and river‑network scales. Integrate, clean, manage, and analyze heterogeneous observational, geospatial, remote sensing, modeled, regulatory, and environmental datasets from multiple sources to support interdisciplinary research on energy systems, hydrologic processes, geochemical dynamics, and terrestrial elemental cycles. Build reproducible computational workflows for data synthesis, model development, feature engineering, representation learning, transfer learning, uncertainty assessment, visualization, and scientific interpretation. Evaluate and interpret model outputs in the context of hydrologic, geochemical, geologic, engineering, climatic, land‑use, and anthropogenic controls on energy and environmental systems. Contribute to interdisciplinary research design, technical reporting, project coordination, and communication with collaborators and project partners. Prepare manuscripts for peer‑reviewed publication and contribute to conference abstracts, presentations, reports, and other scholarly products. Mentor and support graduate and undergraduate students in the research group, especially in coding workflows, data analysis, reproducible research practices, and scientific communication. Participate in regular research group meetings and contribute to a collaborative, interdisciplinary, and supportive research environment. Physical Requirements Not Applicable Application Deadline Priority Consideration: 06/22/2026 Equal Opportunity Employer Syracuse University is an equal‑opportunity institution. The University prohibits discrimination and harassment based on race, color, creed, religion, sex, gender, national origin, citizenship, ethnicity, marital status, age, disability, sexual orientation, gender identity and gender expression, veteran status, or any other status protected by applicable law, to the extent prohibited by law. This nondiscrimination policy covers admissions, employment, and access to and treatment in University programs, services, and activities. #J-18808-Ljbffr