The Computational Laboratory for Advanced Manufacturing and Sustainability (CLAMS) is a newly established research group (https://checlams.github.io/) 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 challenges of our time, including separations, pharmaceutical R&D and manufacturing, digital agriculture, carbon neutrality, Food-Energy-Water Nexus, and more. The group is seeking one to two highly motivated and creative individuals to join the group as Graduate Research Assistants (GRAs) starting in Spring or Fall 2022. Undergraduate and master students with a strong background or interest in developing and applying PSE tools (such as mathematical modeling, optimization and control, AI and machine learning, etc.) to solve fundamental problems in advanced manufacturing and sustainability are encouraged to apply. During their PhD studies, the GRAs will be offered full tuition and stipend that covers their education and living expenses.
WHAT YOU WILL DO
• Develop simple, accurate mathematical models for calculating the total carbon footprint of complex chemical process unit operations, especially in separations. For each model, explore its underlying physical and mathematical properties and derive useful constraints/cuts that can be incorporated in an optimization framework and foster its convergence.
• Develop deterministic global mixed-integer (dynamic) optimization algorithm for synthesis and optimization of next-generation separation process network, which may consist of multiple types of unit operations. Implement various techniques to tighten the bounds and relaxations as well as to reformulate the original optimization problem to achieve faster convergence to global optimality. Applications of interest include optimal multicomponent membrane cascade synthesis, wastewater treatment and water desalination, multilayer plastic waste recycle, recovery of critical rare earth metals from electronic waste, etc.
• Develop reinforcement learning (RL)-based optimal control framework for batch crystallization process, the key unit operation in pharmaceutical industry to produce high-purity drug active ingredients. Build state-of-the-art experimental capabilities at CLAMS with leading-edge online process analytical technologies (e.g., FBRM, ATR-FTIR, Raman, laser backscattering) to achieve feedback control. Establish fundamental understanding and theoretical connection between this framework and traditional population balance model-based optimal control based on Pontryagin's maximum principle.
• Conduct systematic, quantitative life cycle assessment studies on plant-based meat industry, which has been marketed as the future of food as animal meat takes an astonishing amount of water, energy, and land to produce. Identify ways to minimize its water and energy use as well as total carbon footprint. Formulate and solve a life cycle optimization problem to synthesize the optimal plant-based meat manufacturing process and obtain its optimal operating conditions.
• BS/MS degree in chemical/environmental/biological/industrial engineering, mathematics, computer science, or other science and engineering discipline with a strong quantitative background
• Keen interest in PSE research and learning about mathematical modeling, optimization (linear, nonlinear, mixed-integer programming), AI (especially supervised learning and RL), data science and analytics. High self-motivation, enthusiasm, and commitment toward academic research
• Solid background and understanding in linear algebra and good familiarity in mathematical reasoning (e.g., how to construct rigorous mathematical proofs) are necessary
• Good familiarity in using one or more computing software packages and programming languages (e.g., MATLAB, Mathematica, Python, Julia, GAMS, Pyomo) is necessary
• Great verbal and written communication skills using English is necessary
• Familiarity in abstract algebra, real analysis, functional analysis, and partial differential equation is preferred but not required
• Previous exposure to optimization and operations research is preferred but not required
• Experience in parallel computing or high-performance computing is preferred but not required
• Good knowledge of chemical/food process industries is preferred but not required
• Good knowledge of concepts (e.g., Food-Energy-Water Nexus, circular economy) and analysis approaches (e.g., life cycle assessment) related to sustainability is preferred but not required
HOW TO APPLY
Interested candidates can directly contact Prof. Zheyu Jiang at [log in to unmask] with their latest CV and transcript attached. A short written assessment may be sent via email to the candidates who pass the resume screening stage. Upon receipt of the written assessment, candidates will have 48 hours to complete it and to scan and email their answers back. Prof. Jiang may then schedule one or two rounds of virtual interviews with the qualified candidates online via Zoom or Skype. Applications will be accepted on a rolling basis until the position is filled.
We understand that PhD 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. Each candidate who passes the resume and written assessment screenings will receive specific, personalized feedback on his/her interview performance, regardless of the final admission decision.
OSU and CLAMS adhere to a policy that prohibits discrimination on the basis of race, color, sex, sexual orientation, gender identity, religion, creed, national or ethnic origin, citizenship status, age, disability, veteran status, or any other legally protected class. OSU and CLAMS value diversity and seeks talented students from diverse backgrounds. Oklahoma State University is an equal opportunity employer.