Process Systems Engineering Intern

Aspen Technology•Houston, TX

About The Position

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. The Role AspenTech is seeking a highly motivated PhD intern to join our research and development efforts in the Manufacturing & Supply Chain (MSC) group. This internship focuses on pioneering hybrid machine learning and mathematical optimization approaches to address Crude Scheduling Optimization (CSO) challenges at industrial scale. This role is ideal for a student passionate about process systems engineering, operations research, optimization algorithms, and AI-driven decision-making. You will work closely with senior researchers and developers to advance a novel framework that blends fast linear programming (LP) approximations with sequential optimization methods such as model predictive control, reinforcement learning, Bayesian optimization, or related techniques. The intern will have the rare opportunity to contribute to both cutting‑edge research and real-world industrial applications used by global refineries. Your Impact 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 solution‑improvement 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. What You'll Need 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 bi‑level optimization, Understanding of graph theory or network flow optimization, Object‑oriented programming experience, Familiarity with industrial process models or scheduling workflows. Experience with industrial‑scale 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. #LI-WJ1 AspenTech is a global software leader helping industries meet the increasing demand for resources from a rapidly growing population in a profitable and sustainable manner. Our Digital Grid Management software suite, including AspenTech OSI products, helps power and utilities companies achieve superior real-time control, optimization and management for exceptional performance of complex energy networks. If you're looking to make a difference every day and push the limits of performance, AspenTech is doing things no one else thought was possible. As a leading industrial software partner, we help companies all over the world run safer, greener, longer and faster. With over 3700 employees and more than 60 global locations, AspenTech is meeting today's sustainability and business challenges head-on with unmatched expertise, cutting-edge AI-powered technology and a passion to innovate. AspenTech is an Equal Opportunity/Affirmative Action employer. AspenTech does not discriminate against employees or applicants on the basis of age, race, color, religion, creed, ancestry, sex, sexual orientation, gender identity or expression, pregnancy or related conditions, marital status, familial status, national origin, disability, medical condition, genetic information, citizenship, military service or protected Veteran Status or any other basis protected by applicable federal, state, or local law. Reasonable Accommodation: We will provide reasonable accommodations to qualified individuals who have a disability or sincere religious reasons to request accommodation, when necessary to enable the individual to participate in the job application or interview process. If you wish to request an accommodation, please contact us at [email protected]. GDPR Privacy Notice: AspenTech collects a range of personal information during the recruitment process. This may include the following personal or special categories of personal data: recruitment information such as your application form and resume, references, qualifications and membership of any professional bodies and details of any pre-employment assessments; your contact details and date of birth; your gender; your marital status and family details; your identification documents including passport and driver's license and information in relation to your immigration status and right to work with us; information about your contract of employment (or services) including start and end dates of employment, role and location, working hours, details of promotion, salary (including details of previous remuneration), pension, benefits, and holiday entitlement; your racial or ethnic origin; any criminal convictions and offences. AspenTech Security and Privacy Policy Plan Participants Enrolled in the AspenTech US Medical Plans: The Transparency in Coverage Final Rules require certain group health plans to disclose on a public website information regarding in-network provider rates and historical out-of-network allowed amounts and billed charges for covered items and services in two separate machine-readable files (MRF’s). The MRF’s for the benefit package options under AspenTech’s US Employee Benefit Plan are linked below: Transparency in Coverage Rule - Machine Readable Files Transparency In Coverage Rule And Consolidated Appropriations Act Overview and FAQS

Requirements

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).

Nice To Haves

Experience with bi‑level optimization
Understanding of graph theory or network flow optimization
Object‑oriented programming experience
Familiarity with industrial process models or scheduling workflows.
Experience with industrial‑scale 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.

Responsibilities

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 solution‑improvement 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.

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