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Worcester Polytechnic Institute

Just Landed

Research Associate (Postdoctoral Research Associate in Machine Learning and Agentic AI for Chemical and Environmental Engineering) Worcester Polytechnic Institute in United States

Degree Level

Postdoc

Field of study

Computer Science

Funding

Salary: $55,000 to $65,000 per year.

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Country

United States

University

Worcester Polytechnic Institute

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Keywords

Computer Science
Environmental Science
Chemical Engineering
Mathematics
Process Engineering
Uncertainty Analysis
Digital Twin Technology
Reaction Engineering
Optimisation
Data-driven Modeling
ML

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About this position

Worcester Polytechnic Institute is seeking a postdoctoral Research Associate in the Department of Chemical Engineering to work on machine learning and agentic AI methods for chemical and environmental engineering applications.

The successful candidate will collaborate closely with chemical engineers to build hybrid physics- and data-driven models, digital twins, and autonomous workflows for complex reaction and process systems. The role sits at the intersection of engineering science, computational modeling, and modern AI, with a strong emphasis on applying data-driven approaches to physical systems and improving how reaction and process systems are modeled and analyzed.

The project environment is centered at WPI, a research-active institution with state-of-the-art facilities and a broader community that supports interdisciplinary work. The appointment is framed as a postdoctoral research role and is aimed at candidates who can contribute to advanced modeling, simulation, optimization, and AI-enabled workflows in chemical and environmental engineering.

Funding details

Salary: $55,000 to $65,000 per year.

What's required

A Ph.D. in Chemical Engineering, Engineering, Applied Mathematics, Data Science, Computer Science, Operations Research, or a closely related field is required. Applicants should have demonstrated experience applying machine learning to physical, engineering, or scientific systems, strong proficiency in Python and modern machine learning frameworks, experience developing regression, surrogate, optimization, or uncertainty quantification models for engineering applications, and a publication record in machine learning, scientific computing, process systems engineering, or a related field. Preference is given to candidates with experience in digital twins, mechanistic or first-principles model integration, scientific machine learning, physics-informed ML, Bayesian methods, uncertainty quantification, process simulation software such as Aspen Plus, gPROMS, COMSOL, or Cantera, LLMs or agentic AI for scientific workflows, TEA, LCA, process optimization, reaction engineering, and related process modeling and simulation research.

How to apply

Attach a CV, cover letter, and contact information for three references through the application link.

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Review drafts and confirm requirements with the university.