Join Us
We have an opening for a theory postdoc scholar to explore how key aspects of nature shape the diversity-stability paradox in ecosystems - please see the ad here.
We also have an opening for a funded PhD student interested in ecological theory, food web modelling, or population dynamics. Looking for students with strong interests in fundamental ecology/biology (required) and course or research experience in math and modelling (not required). Here are some research directions our lab is interested in.
For prospective graduate students:
Graduate students in the lab use dynamical models as their primary methodology (i.e., using mathematical modeling and computer simulations). This may entail a primarily theoretical thesis or a thesis that combines dynamical models, statistics, and existing data to answer questions in a specific system. Students pursue their own research questions, so having some prior research experience is required. Graduate students typically apply for the NSF graduate research fellowship. Applicants would also be expected to have a strong background in data science or mathematical modeling. We also all actively work to create a supportive, inclusive, and collaborative atmosphere where lab members learn from, help, and inspire each other.
Selecting an advisor that suits your interests and needs is critical to being happy and successful. We hope your interests include one or more of the following: ecosystem resilience, population dynamics, marine ecology, and questions involving time series or spatial pattern analysis. To start a conversation, please drop Vadim an email with:
- Your CV
- An unofficial transcript
- Research interests (including why you want to work in this lab specifically)
We are a new lab and have a wonderful mathematical biology community at UMD comprising the labs of Bill Fagan (theoretical ecology, animal movement), Joshua Weitz (microbial ecology, epidemics), Emme Bruns (plant-pathogen eco-evo), Phillip Johnson (immune dynamics), Abba Gumel (math, epidemics), and Michelle Girvan (physics, networks).