All Open Positions — Research Match

Professor Barber

We research how chemicals in the atmosphere, like wildfire smoke and other pollutants, evolve and interact with air and surfaces. Our group does lots of different kinds of work, but working in our lab will involve complex instrumentation and advanced data analysis processes.

Required

Eager to learn new skills and undaunted by learning independently

Preferred

Students who do well in our group often have strong mathematical skills and/or prior experience with coding for data analysis.
Chemistry or Atmospheric and Oceanic Science major, or other demonstrated interest in those fields
We are most interested in mentoring students who can commit to working in the group for multiple years, and these students are typically sophomores.

Professor Day

We research how wind shapes landscapes on Earth, Mars, Venus, and other planetary bodies to uncover the universal physics of wind-driven sediment transport. Working in our lab will involve collecting field data, running wind-tunnel experiments, processing satellite imagery, or working with numerical models.

Required

No prior background in geology or planetary science is required; reliability, attention to detail, and a willingness to learn our methods matter most.

Professor Dong

We research climate dynamics, like the atmosphere-ocean interactions and tropics-extratropics teleconnections, and how these dynamics are coupled with the Earth’s radiative responses and climate sensitivity to anthropogenic and natural forcings.

Required

Strong background in math, physics, and quantitative analysis
Interested in Earth system modeling and the fundamental physical mechanisms governing the climate system
Prior experience with coding (e.g., Python) and analyzing large numerical datasets

Preferred

Background in Atmospheric and Oceanic Sciences

Bonus

Interested in applying AI/ML approaches to climate science

Professor Diaconescu

We research redox-switchable catalysis, where external agents control a catalyst's oxidation state to tune its activity and selectivity, with applications like controlled synthesis of multiblock copolymers. Our distinct approach uses a unique chelating ferrocene ligand platform that exploits subtle electron-transfer and donor-acceptor interactions to control metal-center reactivity.

Required

Highly motivated and reliable, learns new techniques quickly
Strong background in chemistry
Can commit to working in the group for more than 15 hours per week and more than two years

Professor Hauser

We creates virtual reality simulations for scientific visualizations, mainly for physics and astrophysics, but also for math or quantum computing. View our "published" simulations at the link below.

Required

We are looking for students who are good programmers, ideally in C++ (Python is less ideal).

Preferred

Major in Physics, Astrophysics, Computer Science, or Math
Sophomores or juniors are generally the best fit.

Bonus

Experience with data analysis, visualization, or the Unity game development engine are a plus.

Professor Lithgow-Bertelloni

We research the interior of the Earth and its impact on surface processes. We have also developed techniques that combine particle image velocimetry and thermometry to visualize flow in very viscous fluids. Projects vary from lab experiments to image processing to computer modeling.

Required

Enthusiastic and self-directed learners

Preferred

Some background in Earth and planetary sciences and strong quantitative skills are preferred, but not essential for all projects.

Professor Maynard

We research the frontiers of polymer chemistry and medicine, spanning protein-polymer conjugates, glucose-responsive drug delivery systems, and biomimetic materials inspired by natural molecules.

Required

Completed Chem 30A-C series and the relevant laboratories
Interested in both polymer chemistry and high throughput experimentation techniques.

Professor Cheng

We develop data-centric tools, such as artificially generated tables, which enable privacy-preserving data sharing, with applications in digital marketing, finance, and healthcare sectors. On the theoretical front, we focuses on the emerging field of "Generative Data Science," which elucidates the underlying principles behind generative AI.

Required

Senior students from Computer Science, Math, or Statistics / Data Theory majors
Prior research experience with machine learning
Can contribute 10-20 hours per week