Srivastava Lab

Self-Assembly of Soft Materials

The Srivastava Lab is based in the Chemical and Biomolecular Engineering Department of the Henry Samueli School of Engineering and Applied Science at the University of California, Los Angeles (UCLA).

We harness self-assembly as a tool for soft materials design. We are specifically interested in the role of electrostatic interactions, and our work spans from molecular design and synthesis to fabrication characterizations of nano-, micro- and macro-scale materials, with applications in consumer products (such as cosmetics, adhesive and coatings), biomedical and biochemical industries, construction materials, and 3D printing.


We are building a multidisciplinary team of researchers. Currently, we are looking for graduate students to join our group. 

Graduate Student Opening in the Srivastava Lab

Electrostatic Hydrogel-based Wet Adhesives: We are looking to hire 1-2 graduate (MS and Ph.D.) students for an ongoing NSF-funded project on electrostatic hydrogel-based wet adhesives. This experimental research project will envisage polymer synthesis, material characterization, and application in diverse biomedical settings and will offer ample opportunities for collaborations with biomedical scientists and clinicians. Students with a background in chemical engineering, materials science, chemistry and biochemistry, and physics are encouraged to apply. Interested candidates should email Samanvaya at

Graduate Student and Postdoctoral Researcher Openings in the Srivastava Lab

Smart Water Treatment Systems: We are looking to hire 2 graduate (Ph.D.) students and two (2) postdoctoral researchers for an industry-collaborative project on smart water (pre)treatment systems. This research project will envisage the assembly and operation of water (pre)treatment systems along with process modeling and developing model-predictive control strategies to enable remote system operations. This project will offer ample opportunities for work in the lab and the field, as well as collaborations with industry collaborators. Ph.D. graduates (or soon-to-be graduates) with a background in chemical engineering, water treatment, and machine learning-based process modeling and control are encouraged to apply. Interested candidates should email Samanvaya at

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