PhD /MS Assistantship in Spatial Hydrology or Riverscape Health

Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI); Arlington (MA); On-site; USD 90,000 - 120,000; Full time

Job summary

Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) in Arlington, MA seeks a PhD-level researcher to advance Earth system science through process-based and data-driven hydrologic modeling.

You will apply and develop large-scale models (SWAT, ParFlow) and contribute to ecosystem-level analyses of carbon and water cycling. Ideal candidates have strong programming (Python/R), statistics, and a track record of independent research plus collaborative teammates.

Qualifications

• PhD in hydrology, ecology, soil and water science, environmental science, engineering, or related field.
• Strong quantitative and computational skills with experience in process-based and numerical modeling, especially hydrological modeling.
• Research experience or strong interests in Earth system processes, carbon and water cycling; interest in AI-enabled hybrid modeling.
• Proficiency in programming and statistics, preferably Python and R.
• Strong self-motivation and independent research ability with teamwork and interpersonal skills.

Skills

Quantitative skills

Computational modeling

Independent research

Teamwork

Self-motivation

Earth system science

Data analysis

AI-enabled hybrid modeling

Knowledge-guided machine learning

Programming

Python

R

Education

PhD in hydrology, ecology, soil and water science, environmental science, engineering, or related field

Tools

SWAT

ParFlow

ATS

Python

R

Job description

• A PhD in a relevant field (e.g., hydrology, ecology, soil and water science, environmental science, engineering, or related fields).
• Strong quantitative and computational skills, with demonstrated experience in process-based and numerical modeling (e.g., biophysical, ecosystem, watershed), especially hydrological modeling (e.g., SWAT, ParFlow, ATS), and large-scale data analysis and synthesis.
• Research experience or strong interests in Earth system processes, particularly carbon and water cycling, and interests in AI-enabled hybrid modeling and knowledge-guided machine learning are highly preferred.
• Proficiency in programming and statistical experience, preferably in Python and R.
• Strong self-motivation and independent research ability with teamwork and interpersonal skills.