OPPORTUNITIES
We are hiring!
Seeking an outstanding postdoctoral candidate to contribute to an innovative biological methane-capture technology, with future opportunities to support its commercialization and assume a leadership role in the resulting startup. Learn more about the opportunity here.
ADDITIONAL PROJECTS
For other projects, although we are not actively looking for new postdocs, exceptional candidates will be considered. Candidates should send their CV and a description of their research interests by email. The following projects are listed in order of priority for hiring.
Multiscale Responsive Kinetic Modeling (MsRKM)
MsRKM is a kinetic modeling framework that integrates simulation and experimental data to describe equilibrium, steady-state, and non-equilibrium processes. Our current focus is on electrochemically driven transport, making the approach particularly well suited for studying channels and transporters—one of our most exciting research directions. This project will involve both methodological development and application to ion channels and transporters initially, with the potential to expand to a wide range of additional processes over time.
The ideal candidate will have prior exposure to kinetic modeling (e.g., microkinetic models, systems biology models, Monte Carlo kinetic modeling, or Markov state models), as well as simulations, enhanced sampling techniques, and machine learning. Familiarity with channels and transporters is advantageous, and opportunities for learning on the job are anticipated.
Multiscale Simulations for Methane Conversion
This project investigates the roles of membranes in the uptake, delivery, and oxidation of methane by pMMO. The work will involve large-scale molecular simulations, enhanced sampling methods, and potentially multiscale techniques such as reactive MD and/or QM/MM, enabling mechanistic insights across relevant spatial and temporal scales.
The ideal candidate will have experience with molecular dynamics and familiarity with some of the advanced methods noted above, with opportunities for further training fully supported. An interest in applying machine-learning approaches is also welcome.
Protein Targeting to Lipid Droplets
This project aims to elucidate the mechanisms by which proteins specifically target lipid droplets and how these interactions are regulated in response to cellular conditions. Our goal is to uncover the regulatory principles governing the lipid-droplet proteome. To achieve this, we employ all-atom and coarse-grained simulations and are developing new computational methods to characterize these processes.
The ideal candidate will have experience with molecular dynamics and a strong interest in biomembrane biophysics, with opportunities for additional training fully supported. Interests in kinetic modeling, systems biology, or machine learning are also welcome.