We combine analytical mechanics with statistical physics to understand how complex systems fail, grow, and move. The lab spans fracture in disordered solids, instability in soft materials, and the force-generating machinery of living cells — connected by a shared toolkit of bifurcation theory, mean-field models, and finite-element simulation.
We are actively recruiting at all levels. If you are drawn to problems where mathematical rigor meets physical insight, read on.
Current openings are centered on three broad directions. The best projects emerge from conversation between student interests and lab directions — these are starting points, not fixed assignments.
What do the statistics of small, localized events — acoustic emissions, micro-cracks, dislocation bursts — tell us about imminent catastrophic failure? We develop theoretical frameworks, grounded in statistical physics, to answer this question across a range of material systems: from disordered brittle solids to biological networks. The goal is both fundamental understanding and practical prediction.
Soft materials — gels, elastomers, biological tissues — fail in ways that differ fundamentally from hard solids. Rather than a single propagating crack, failure often involves a symmetry-breaking instability that selects among competing fracture patterns. We develop analytical and computational tools to understand this selection process, with applications ranging from hydrogels to soft biological media under fluid pressure.
Living cells actively generate and sense mechanical forces through a thin layer at their surface — the actomyosin cortex. We build theoretical models of this active material to understand how cells change shape, divide, and migrate. Projects in this direction connect to biology at multiple scales: from the mechanics of a single molecular motor to the coordination of forces across an entire tissue.
The lab's toolkit is broad by design. Students leave with skills that are rare in engineering departments and valued across academia and research.
Deriving closed-form stability conditions, performing asymptotic expansions, and classifying instabilities — the mathematical core of the lab's work.
Mean-field theory, avalanche statistics, universality, and critical phenomena — reframed as tools for understanding mechanical failure and biological systems.
Implementing custom constitutive models in FEniCS, variational formulations, and scientific Python. All students build computational fluency in the lab.
Writing for and presenting to audiences in mechanics, physics, and biology. The lab publishes in PRL, JMPS, Cell, and Nature-family journals.
Admitted through the CEGE graduate program at the University of Minnesota. Students develop an independent research direction within the lab's themes over the course of their PhD.
We welcome candidates with expertise in computational mechanics, statistical physics, soft matter, or biological physics. Interdisciplinary and unconventional backgrounds are genuinely valued.
UMN undergraduates interested in research for academic credit (UROP), summer projects, or early-stage research experience are welcome to reach out directly.
The lab has a distinct style — honest self-selection saves everyone time.
Yes — please do. Send a brief email to hudsonbr@umn.edu describing your background and which direction interests you most. We can discuss fit before you invest time in a full application. Formal applications go through the CEGE graduate program.
Absolutely. The lab is deliberately cross-disciplinary. What matters is mathematical fluency and genuine curiosity — not which department you trained in.
Useful but not required. All students learn FEniCS and scientific Python in the lab. We look for the analytical intuition that lets you use those tools meaningfully.
Primarily theoretical and computational. Core projects involve analytical mechanics and finite-element simulation. Some projects include tabletop experiments or collaboration with experimental groups, but we do not run a large experimental facility.
Hands-on early, progressively more independent. The first year involves close collaboration on a defined project to build skills and intuition. By year three, students are expected to drive their own research directions. Weekly one-on-one meetings are the norm.
Send a short email introducing yourself — your background, what draws you to the lab, and which direction interests you most. No attachments required at this stage.