Fully funded PhD positions as well as a postdoc fellowship in AI‑Driven Soft Materials Design are available in the CMPD group at the University of South Carolina. The group is headed by Dr. Shengli (Bruce) Jiang, who will start his tenure-track faculty position as assistant professor in the Department of Chemical Engineering at the Molinaroli College of Engineering and Computing, USC, in Fall 2026.
This is a great chance for those who are looking into computational materials science, polymer physics, molecular simulation, and machine learning applications to energy and circular economy.
About the AI‑Driven Soft Materials Design Position
The Computational Materials and Product Design Group specializes in the development of soft materials for use in energy and circular economy contexts. The group combines molecular simulations, machine learning (including generative and topological/deep learning), and theoretical approaches to create functional materials and material-derived products.
Key Research Areas for AI‑Driven Soft Materials Design
- Upcycling of plastic and circular polymers.
- Electrolyte polymers and ion-conducting membranes.
- Soft materials in complex fluids and processes through multiscale modeling.
Students and researchers will collaborate in an interdisciplinary space where chemical engineering, materials science, physics, and data-driven approaches intersect.
AI‑Driven Soft Materials Design Funding Benefits
According to recruitment details from the group, the openings are for fully funded PhD studentships and a paid postdoctoral researcher position. If selected, candidates will receive:
- Full tuition waiver (through graduate assistantship or equivalent departmental funding).
- Competitive salary/payscale for maintenance, comparable to USC standards and department guidelines.
- Opportunity to work in USC’s research labs with the company of engineers and computer scientists.
(The exact amounts of stipends and terms are determined by the department and the university.)
Eligibility and Preferred Background
PhD Applicants
- BSc or MSc degree in chemical engineering, materials science, physics, applied mathematics, or a related discipline.
- Interest in computation-related topics, for example, molecular simulations, machine learning, and/or theoretical modeling.
- Computational experience is desirable but not necessary; motivated individuals who are keen on acquiring computational skills are welcome to apply.
Postdoctoral Researcher
-
PhD (either completed or close to completion) in an appropriate field like chemical engineering, material sciences, physics, or any other relevant discipline.
-
Experience in at least one or more of the following fields: molecular modeling & simulations, statistical mechanics / meso-scale modeling, generative AI/geometry topology deep learning, physics-informed machine learning, or polymer sciences / complex fluids.
KTH Postdoc: 1 funded Position in Biobased Battery Separators
How to Apply
Please email all materials as a single PDF to [email protected] using the appropriate subject line:
- PhD applicants:
[Prospective Ph.D. - Your Name] - Postdoc applicants:
[Prospective Postdoc - Your Name]
PhD applicants – include:
- CV
- one‑page cover letter (experience, research interests, intended start term)
- Transcripts (unofficial copies accepted initially)
- Contact information for two references
Postdoc applicants – include:
- CV
- one‑page cover letter (experience, research interests, intended start term)
- one or two representative publications
- Contact information for three references
Potential candidates are advised to make contact with the group promptly, particularly if planning to join in Fall 2026 or Spring 2027.
Why This Opportunity Matters
This is an excellent choice for candidates who wish to work on the frontier between soft materials, energy storage, sustainability, and AI‑driven soft materials design. Here, one has the opportunity to develop expertise in computer simulation techniques and machine learning while addressing real-world issues related to energy and the circular economy.
I have posted this announcement on my scholarship blog in order to provide information for individuals who are interested in computational materials science and sustainable technologies on well-funded paths towards graduate school or a postdoctoral position. If this matches your interests or those of someone you know, I encourage you to contact Dr. Jiang directly via her group’s website.
AI‑Driven Soft Materials Design Position FAQ
1. Who is hiring you?
The CMPD Group, headed by Dr. Shengli (Bruce) Jiang from the University of South Carolina, Department of Chemical Engineering, is hiring for these openings.
2. Which positions are available?
Fully funded PhD student positions and at least one position for a postdoc who works with AI‑driven soft materials design and more.
3. How soon can I start?
For PhD students, positions are planned to commence starting fall 2026 and spring 2027, and for the postdoc, the commencement date will be negotiable.
4. What type of research will I conduct?
My research interests are in the development of soft materials for sustainable energy and circular economy purposes, which include plastic upcycling/circular polymers, polymer electrolytes/ion-conducting membranes, and multiscale modeling of soft materials in complex fluids.
5. Which types of techniques will I employ?
I would use techniques such as molecular modeling/simulation, machine learning/artificial intelligence (AI) techniques, including generative modeling, geometry, and topology and theoretical modeling of soft materials.
6. Is there any academic qualification necessary to apply for PhD positions?
Applicants should have either a bachelor’s or master’s degree in chemical engineering, materials science, physics, applied mathematics, or another relevant field. Computational experience is beneficial but not absolutely necessary.
7. What background would you prefer in the postdoctoral researcher?
The applicant should have a PhD in a relevant field (or be close to defending it) and preferably have experience in one or several of the following topics: molecular simulations, statistical mechanics/mesoscale modeling and machine learning for materials and/or polymer science.
8. Is the position funded?
Yes, PhD positions are fully funded (tuition and stipends through assistantships or similar funding mechanisms), while the postdoc is a funded research position.
9. Do you require prior programming and machine learning knowledge for computational projects?
Some knowledge of programming or machine learning algorithms is beneficial, but we would consider applications from highly motivated candidates willing to learn new skills as well.
10. Could an international applicant apply?
Yes, although we would expect international applicants to have their qualifications checked against the US requirements through USC graduate program pages.
11. How do I apply for the PhD positions?
Students who are interested should first contact Dr. Jiang’s group with their CV and a statement of research interests, then submit their PhD applications to the University of South Carolina’s Graduate School.
12. How do I apply for the postdoctoral position?
Interested candidates should send their CV, a cover letter explaining their research interests and the dates they can start working, as well as the references’ contact information, to the email address stated on the CMPD Group website ([email protected]).
13. Are there any deadlines?
The deadlines might be based on the usual timeline of applying for a PhD at the University of South Carolina, but candidates are highly recommended to contact Dr. Jiang early since the positions could be taken anytime.
14. Where can I get more information?
All information will be found on the CMPD Group website (www.shenglijiang.com) and the USC Chemical Engineering Department website.
Leave a Reply