Postdoctoral Researcher - Optimization with Embedded Machine Learning Surrogates

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Why Join ExxonMobil?

At ExxonMobil, we apply advanced optimization and machine learning techniques to solve some of the most challenging problems in energy, manufacturing, and low-carbon technologies. In this role, you will work on cutting-edge methods at the intersection of OR and AI, directly impacting critical business decisions and shaping next-generation computational decision-support capabilities.

About the Role

ExxonMobil is seeking a highly motivated Postdoctoral Researcher specializing in the integration of mathematical optimization and machine learning through surrogate modeling.

This role focuses on embedding ML-based surrogate models directly within optimization frameworks to enable efficient decision-making for large-scale, high-value business applications. A key challenge lies in balancing surrogate model fidelity with optimization tractability and developing scalable solution algorithms for resulting nonconvex and large-scale formulations.

The ideal candidate is a recent Ph.D. graduate with strong expertise in operations research, mixed integer linear or nonlinear optimization, and machine learning, with interest in solving real-world industrial problems involving complex physical systems.

Key Responsibilities

• Develop optimization frameworks with embedded ML-based surrogate models for complex systems.
• Design and implement formulations that integrate neural networks and other surrogate models into optimization problems (e.g., MIP, MINLP, and nonconvex programs).
• Investigate trade-offs between surrogate model fidelity and optimization tractability.
• Develop specialized solution algorithms for challenging problem structures, including bilinear and nonconvex formulations.
• Explore hybrid solution approaches combining:
• Mathematical programming (e.g., MIP/MINLP)
• Gradient-based optimization (e.g., SLSQP)
• Derivative-free optimization (e.g., NOMAD)
• Leverage tools such as GurobiML, OMLT, and decomposition methods
• Apply developed methods to high-impact business problems across upstream, downstream, and low-carbon solutions.
• Communicate results through technical reports, publications, and presentations.

Example Research & Application Areas

• Optimization with embedded neural network surrogates
• Learning-based surrogate modeling for physics-based systems
• Nonconvex and bilinear optimization arising from ML model integration
• Difference-of-convex (DC) programming and relaxations
• Gradient-based vs. derivative-free optimization strategies
• Hybrid optimization algorithms combining ML and OR

Required Qualifications

• Ph.D. in Operations Research, Industrial Engineering, Applied Mathematics, or a closely related field.
• Strong background in mathematical optimization, including nonlinear and mixed-integer optimization.
• Demonstrated research experience in at least one of the following:
• Optimization with embedded machine learning models
• Surrogate-based optimization
• Nonconvex or bilinear optimization
• Knowledge of machine learning models used for surrogate modeling (e.g., neural networks, regression models).
• Strong programming skills in Python.
• Experience with optimization solvers (e.g., Gurobi, CPLEX, IPOPT).
• Strong analytical, problem-solving, and communication skills.
• Ability to work in multidisciplinary teams with domain experts.

Preferred Qualifications

• Experience with tools such as GurobiML, OMLT, or similar ML-to-optimization frameworks.
• Experience with derivative-free optimization methods (e.g., NOMAD, Bayesian optimization).
• Knowledge of gradient-based nonlinear optimization methods (e.g., SLSQP).
• Experience working with large-scale industrial or engineering systems.
• Understanding of surrogate model training and validation trade-offs.
• Strong publication record
• Experience developing reusable optimization frameworks or toolkits.

Desired Attributes

• Interest in solving complex, large-scale industrial decision problems.
• Ability to balance model fidelity, scalability, and computational performance.
• Strong collaboration skills with both technical and domain experts.
• Self-driven with the ability to independently lead research initiatives.

Duration

This opportunity is for a postdoctoral position expected to last one to three years, subject to annual review and renewal.

Work Location

This post doctoral research position will be located at our main corporate office in Spring, Texas.

Your Total Rewards

An ExxonMobil career is one designed to last. Our commitment to you runs deep: our employees grow personally and professionally, with benefits built on our core categories of health, security, finance, and life. Individual pay is determined based on various factors including degree/education, discipline, year of study, skills, abilities, qualifications, and work experience.

More information on our Company's benefits can be found at .

Please note pay rates and benefits may be changed from time to time without notice, subject to applicable law.

Relocation Options

Relocation benefits may be available to you based on ExxonMobil eligibility guidelines.

Equal Opportunity Employer

ExxonMobil is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, sexual orientation, gender identity, national origin, citizenship status, protected veteran status, genetic information, or physical or mental disability.

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