Process Systems Engineering Intern

The driving force behind our success has always been the people of AspenTech. What drives us, is our aspiration, our desire and ambition to keep pushing the envelope, overcoming any hurdle, challenging the status quo to continually find a better way. You will experience these qualities of passion, pride and aspiration in many ways - from a rich set of career development programs to support of community service projects to social events that foster fun and relationship building across our global community.

• Formulate and analyze optimization models for crude scheduling, including LP-based relaxations and sequential refinement strategies.

• Design and implement optimization and/or machine learning components (e.g., MPC, RL, Bayesian optimization) to explore solutionimprovement workflows.

• Develop prototype computational workflows to evaluate hybrid optimization pipelines.

• Conduct numerical experiments comparing speed, robustness, and accuracy across solution strategies.

• Investigate performance tradeoffs between traditional CSO and hybrid approximate approaches.

• Document research findings, prepare internal reports, and present results to senior technical staff.

• Collaborate with AspenTech researchers, software developers, and domain experts.

• Current PhD student in: Process Systems Engineering, Operations Research, Industrial Engineering, or a closely related field.

• Strong interest and background in formulating and solving optimal scheduling and/or planning problems.

• Familiarity with sequential optimization approaches, such as: Reinforcement Learning, Model Predictive Control (MPC), Bayesian Optimization.

• Fluency in modeling and solving optimization problems in at least one language/platform, such as: Pyomo, GAMS, JuMP, AMPL, Python, Julia, MATLAB, C++ (optimization focus).

• Preferred additional skills: Experience with bilevel optimization,

  Understanding of graph theory or network flow optimization,

  Objectoriented programming experience,

• Familiarity with industrial process models or scheduling workflows.

• Experience with industrialscale optimization in a real-world refinery scheduling context.

• Hands-on exposure to machine learning + optimization hybrid algorithms.

• Mentorship from technical leaders in AspenTech's optimization and scheduling technology teams.

• Opportunity to contribute to potential publications or future product features.

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Hourly Internship Pay Rate: $40.00 - $50.00

Work Location:

Aspen Technology has a global hybrid workplace for most of our roles. Employees are expected to work in the office 4 days per week and may work remotely 1 day per week, unless the employee has a reasonable accommodation or other approved exception to the hybrid workplace schedule. Our Internship role is based in our Houston, Texas office.

Work Authorization

AspenTech will only employ those who are legally authorized to work in the United States. This is not a position for which sponsorship will be provided. Individuals with temporary visas such as E, F-1(including those with OPT or CPT) , H-1, H-2, L-1, B, J or TN, or who need sponsorship for work authorization now or in the future, are not eligible for hire.