We are seeking a Postdoctoral Associate to lead a project on the seasonal dynamics of community composition under environmental change. This position will contribute to understanding how phenological shifts at the population level scale up to affect seasonal dynamics of community composition and ecosystem functions. The project will leverage long-term high-resolution community composition datasets (e.g., phytoplankton). Analyses may include ordination, time series modeling, and machine learning. This is an opportunity to contribute to theoretical frameworks and to publish high-impact papers through the synthesis of big data. There will also be opportunities to collaborate with internal and external partners, co-mentor students, and contribute to grant development. Preferred start date will be late January or February 2026.

Preferred domain knowledge:

● Strong background (Ph.D) in ecology or a related field, with interest or experience in community structure and/or species interactions

● Experience investigating ecological patterns over time

● In-depth knowledge of at least one study system (e.g., freshwater phytoplankton)

Preferred technical skills:

● Proficiency in multivariate statistics, time series analysis

● Proficiency in R and Python for ecological data analysis

● Ability to manage and synthesize large ecological datasets (e.g., observatory networks, remote sensing, or environmental sensor data)

● Familiarity with network analysis, Bayesian modeling, or machine learning would be beneficial

This is a full‑time, in‑person position based on the Pittsburgh campus. The start date will be January 2026, with flexibility.