PhD opening in systems engineering/applied math/AI/optimization

The Computational Laboratory for Advanced Manufacturing and Sustainability (https://checlams.github.io/) is a young research group led by Dr. Zheyu Jiang, assistant professor in chemical engineering at Oklahoma State University. At CLAMS, we develop systems engineering solutions to tackle some of the most pressing and interdisciplinary challenges, including industrial decarbonization, digital agriculture, and food-energy-water nexus. The group is currently seeking one to two highly motivated and creative students to join the group as Graduate Research Assistants (GRAs) starting in Fall 2025 or Spring 2026. The detailed posting can be found on our group website.

WHAT YOU WILL DO

• Multi-scale modeling: You will develop efficient neural solvers and physics-informed neural networks for accurately solving ordinary and partial differential equations that model complex physiochemical phenomena and processes in various engineering and sustainability applications, such as water infiltration in soil for digital agriculture, CO₂ transport in shale reservoirs for carbon sequestration and storage, electrolyzer and fuel cell modeling for clean energy production, and so on.
• AI for Science: You will develop digital twin solutions combining physics-based first principles with data-driven techniques for applications such as variable renewable energy (e.g., solar and wind) forecasting, electricity market price prediction, and battery systems prognosis and optimization. Meanwhile, you will develop computational efficient and scalable algorithms to solve inverse problems (e.g., parameter estimation and uncertainty quantification) for these engineering and environmental applications.
• Optimization: You will develop deterministic and/or stochastic optimization models, implement convexification, reformulation, and decomposition techniques, and design efficient algorithms to solve critical problems such as: 1) privacy-preserving decentralized optimization algorithms for joint operation, maintenance, and planning of modern energy systems; and 2) designing resilient infrastructures and supply chains for food and chemicals (e.g., plastic waste and battery waste upcycling) using distributionally robust optimization.
• Explainable AI: By leveraging latest advancements in explainable AI, develop new AI/ML architectures and platforms to enhance interpretability and explainability, improve accuracy, ensure underlying physics, and/or preserve data privacy of classic AI/ML methods in solving problems related to clean energy technologies and digital agriculture.
• Safe reinforcement learning (RL) and optimal control: You will develop new theories and algorithms in safe RL to smoothly integrate hard safety constraints RL with provable convergence and optimality properties for optimal control of batch crystallization process for pharmaceutical/agrochemical manufacturing, greenhouse/building thermal systems, agricultural irrigation, etc.

QUALIFICATIONS

• Although the lab is housed in the School of Chemical Engineering, due to the multidisciplinary nature of our work, we seek BS/MS candidates from a diverse pool of expertise, including chemical engineering, industrial engineering & operations research, applied mathematics/statistics, and computer science.
• Candidates should have a strong quantitative background and solid understanding of calculus, linear algebra, and probability/statistics. They should be comfortable with mathematical reasoning, such as performing mathematical derivations, writing proofs, coding, and conducting numerical experiments.
• We are looking for candidates who demonstrate self-motivation and commitment toward PhD study as well as strong enthusiasm for learning new topics in mathematical modeling, optimization, AI/ML, and optimal control.
• Good familiarity in one or more scientific computing software packages and programming languages (e.g., MATLAB, Python, Julia, GAMS, Pyomo), as well as open-source machine learning frameworks (e.g., PyTorch).
• Good verbal and written communication skills using English. Proof of English competency can be in the form of an official TOEFL, PTE Academic or IELTS score. Scores must be from an exam taken within the last two years. The minimum requirements are TOEFL 79 iBT, 53 PTE Academic or 6.5 IELTS academic stream.

ABOUT OSU & STILLWATER

Oklahoma State University's official Carnegie research designation is R1: "Very High Spending and Doctorate Production", which is Carnegie's top research designation. Only 187 universities of America's 5900 institutions qualified for this prestigious designation in the latest report. Being a Carnegie R1 research institutions provides opportunities for private and public partnerships and offers opportunities for economic growth in the state. As a land-grant institution, OSU's research projects are aimed at improving the quality of life for Oklahomans and addressing future technology needs.

OSU is located in the safe, inclusive, and friendly college town of Stillwater which has very low cost of living and state-of-the-art research, education, and wellness facilities. Each GRA will receive a monthly stipend of $2,500, plus full tuition waiver for graduate courses and coverage of single-person health insurance premium. Stillwater is one of the few college towns in the U.S. that has an airport operating commercial flights, making domestic and international travel convenient. Oklahoma is one of the fastest growing states in the U.S. with lots of opportunities and great convenience. Stillwater is within 1-hr drive from Oklahoma City and Tulsa, the two largest cities in Oklahoma with outstanding food scenes as well as many indoor and outdoor activities.

HOW TO APPLY

Interested candidates can directly contact Prof. Zheyu Jiang (zheyu.jiang@okstate.edu) with their latest CV and transcript(s) attached. A short written assessment about the candidate’s quantitative skills may be sent via email to candidates who successfully pass the CV screening stage. Prof. Jiang may then schedule one or two rounds of virtual interviews with the qualified candidates before final decisions are made. Applications will be accepted on a rolling basis until the position is filled.

We understand that graduate program application can be a long and arduous experience, which mostly comes from the uncertainty and frustration during waiting. Therefore, we strive to make our interview process as fast and transparent as possible. We offer timely application status update to each candidate to minimize waiting time.