Official announcements for openings
- Postdoctoral Fellow: Control theory principles for microbial therapy design
- Postdoctoral Fellow: Computational Biology (ML4Bio)
We are always looking to grow the lab. If you are interested in our work but not one of the above announcements or the projects below then still reach out (this list will not always reflect all of our openings or projects). Please don’t hesitate to reach out because you think your background may not work. We need engineering, math, computer science, ecology, statistics, chemistry, microbiology, and much more to get these projects off the ground. See our funding for more information on the grants supporting this work. While we are primarily a computational group all students and postdocs are encouraged to design their own experiments, that are then carried out by staff in the host-microbiome center or through our many collaborating labs. Interested graduate students, postdocs, research associates or rotation students should send cover letter and cv to tegibson@bwh.harvard.edu.
Current projects
- Statistical machine learning models for biological time-series and dynamical systems.
- Developing time and quality aware strain tracking models to study the colonization dynamics of pathogens.
- Developing robust microbial consortia (using putative interactions learned from our dynamical systems models and using control theory principles) and testing them in gnotobiotic mice.
- Provably stable and robust gradient descent algorithms for optimizing differentiable ML models with applications in Healthcare and Biology.
- Experimental and computational methods for interrogating gut-brain axis
- Methods for integrating multiple data modalities and prior knowledge (from other studies or databases) in time-series models.
- Host microbiome interactions (dry or wet/dry, see below)
We also have an opening for someone who wants to have some wet lab experience
- Time in both the Gibson Lab and the Walt Lab developing SIMOA assays for the detection of cytokines in feces, and developing ML models for host-microbiome interactions.