Research Scientist

WPI•Worcester, MA

5d•Onsite

About The Position

The Department of Civil, Environmental, and Architectural Engineering at Worcester Polytechnic Institute invites applications for a research scientist in Indoor Airflow, Ventilation, and CFD position beginning in Fall 2026 or Spring 2027. The position will focus on indoor airflow, ventilation, air distribution, indoor environmental quality, and computational fluid dynamics (CFD). The successful candidate will contribute to research that combines CFD modeling, primarily using ANSYS Fluent and OpenFOAM, with laboratory-based measurements and Machine Learning (AI). We are especially interested in applicants with strong CFD experience and an interest in applying modeling and AI tools to real building and indoor environmental problems. Prior experience with laboratory experiments, airflow measurements, ventilation studies, or CFD validation is desirable, but candidates with strong CFD backgrounds and willingness to develop experimental skills are also encouraged to apply.

Requirements

Ph.D. in Architectural Engineering, Mechanical Engineering, Civil Engineering, Environmental Engineering, Aerospace Engineering, or a related field
Strong research experience with CFD, preferably ANSYS Fluent
Knowledge of fluid dynamics, heat transfer, and turbulence modeling
Programming skills for data analysis, model development, or simulation workflows
A record of peer-reviewed publications related to CFD, indoor environments, ventilation, or related areas
Ability to work independently and collaboratively

Nice To Haves

Experience with indoor airflow, ventilation, HVAC, or air distribution modeling
Experience with laboratory measurements or experimental validation
Familiarity with Ansys Fluent and other CFD tools (e.g., OpenFOAM )
Interest in indoor air quality, thermal comfort, or building environmental performance

Responsibilities

Contribute to research that combines CFD modeling, primarily using ANSYS Fluent and OpenFOAM, with laboratory-based measurements and Machine Learning (AI).
Apply modeling and AI tools to real building and indoor environmental problems.