Seed Grant Program

The Center for Quantitative Cell Biology offers seed grants to support our scientists. The purpose of this competition is to help start specific new projects and promote inter-center collaboration that support the Center’s core objectives. To encourage inter-center collaboration, proposals must have at least one QCB collaborator. 

We are looking for great ideas that will propel us further toward our goal of a whole cell model while also making widespread impact.

Funding is for one year. Faculty are funded at either $50,000 or $75,000. Students/postdocs are funded at either $25,000 or $50,000.

Please note: These funds are for direct costs. Indirect costs will be covered by QCB and should NOT be factored into your seed grant budget.

Application is easy, see instructions below. View sucessful proposals here.

Email questions to qcb@beckman.illinois.edu

  • Applications alternate years between grants for faculty and grants for students/postdocs.

  • Faculty applications now open. Apply by February 15, 2026.

  • Student/postdoc applications open late 2026/early 2027.

Application Information


 

Only applicants meeting these criteria will be considered:

– Complete and submit application by the deadline: February 15, 2026. The application consists of the online application form and a one-page proposal. Please note, if you are the PI on a previously awarded QCB Seed Grant, you will not be considered for another one until you have submitted a report on that grant. Also, know that not all proposals may be funded.

– Propose new research in one of the core QCB areas. Applicants with closely aligned research areas will be considered. Core QCB research areas are:

Propose research in one of the core QCB areas. Applicants with closely aligned research areas will be considered. Core QCB research areas are:

    1. Whole Cell Modeling: – improve WCMs for bacteria, yeast and eukaryotic cells and connections to established MD (e.g., Martini, Gromacs, NAMD) and analysis programs (e.g., VMD)
    2. Subcellular Dynamics using MINFLUX: data acquisition and new data/analysis tools
    3. Spatial Chemical Composition using label-free imaging
    4. Converting cryo-electron tomograms into images for WCMs and Minecraft
    5. Studies of synthetic biology and organelles in selected organisms (e.g., yeast).
    6. Development of high-throughput machine learning models and tools for multi-omics and novel microscopy data, spanning methodological development, scalable computation, and scientific inference.