Post Doctoral Research Associate - Paros Center
Listed on 2026-02-16
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Research/Development
Research Scientist, Data Scientist
Overview
Title: Post Doctoral Research Associate - Paros Center
Executive Area: Academic Affairs
College/School/MBU: Engineering
Department: Dean - Engineering
Work Location: Amherst
Schedule: Full Time
Work Arrangement: Hybrid
The Paros Center for Atmospheric Research at the University of Massachusetts, Amherst seeks a Post Doctoral Research Associate to conduct observations-based research, supported by numerical simulations, on high-impact mesoscale and local-scale weather systems. The incumbent will work on analyses of mesoscale processes and their coupling with moist convection to understand hazardous weather in central and eastern North America, in both warm and cool seasons.
Field observations from projects such as NURTURE and Atmospheric River Reconnaissance, and other instrument deployments, will be analyzed alongside numerical simulations to interpret observations and explore hazard predictability. The work will be conducted in collaboration with the Paros Chair for Atmospheric Research (Chris Davis) and other faculty within the Paros Center and the College of Natural Sciences and College of Engineering.
- Conduct original research in collaboration with faculty in the College of Natural Sciences, College of Engineering, and others in the Paros Center for Atmospheric Research at UMass, Amherst.
- Participate in field data collection (as opportunities arise) and perform data quality-control.
- Synthesize observations from different instruments and observing platforms.
- Perform simulations and analysis of weather systems independently with some guidance.
- Produce visualizations of data for analysis and presentation.
- Co-author or lead-author scientific publications and proposals.
- Present research results at conferences and other scientific venues.
- Participate in, and sometimes lead, research discussion meetings with the Paros Chair and other faculty, students, or postdocs.
- Assist in mentoring of undergraduate and graduate students.
- Perform other tasks as agreed upon.
- Ph.D. in atmospheric science or related discipline.
- Experience in observational analysis.
- Experience using numerical models to simulate weather systems.
- Ability to process and analyze large datasets.
- Python programming experience.
- Excellent written and oral communication skills.
- Field observation experience, especially analysis of remote sensing data.
- Working knowledge of AI-enhanced workflows and machine learning techniques to accelerate analysis of models and observations.
- Experience with the Model for Prediction Across Scales (MPAS) or Cloud Model 1 (CM1).
- Priority given to applications received by March 15, 2026.
- Term is 1 year, renewable for a second year, with the possibility of further extension depending on funding.
- Desired start date range is May-June, 2026.
- Position may require travel to field sites and conferences or other scientific meetings.
Work is performed in a standard office or indoor university environment and involves minimal physical exertion.
Work Schedule and Work ArrangementTypically Monday-Friday, 9 am to 5 pm. This position has the opportunity for a hybrid work schedule, defined by the University as an arrangement where an employee’s work is regularly performed at a location other than the campus workspace for a portion of the week.
Salary InformationUMass Amherst expects to pay within an approximate range between $70,000 and $77,000 for this position.
Special Instructions for ApplicantsPlease apply online and upload your CV, cover letter and contact information for three (3) professional references. Your cover letter should include: (1) Why the position appeals to you; (2) Your relevant experience and how it fits with listed skills and aptitudes; (3) How this position fits with your career goals. For any questions, please email Chris Davis, chradavis.
This position will remain open for the time period required by any applicable collective bargaining agreement and will continue until a suitable candidate pool is identified. Interested applicants are strongly encouraged to apply early.
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