Research Scientist

## **About Tabula** Tabula is building an AI-first therapeutics company. We are starting with bacteriophages, natural predators of bacteria, and building the models and experimental systems needed to design better therapies for hard-to-treat infections. The immediate problem matters on its own. Antibiotic resistance is a large and growing global problem, and new approaches are badly needed. But we also think this is the beginning of something broader. Most biotech companies are mostly biologists with a small computational team attached. We think that model is going to look dated. Our view is that drug and therapy discovery will become much more computational over time, and we are building Tabula around that belief from day one. The core of our scientific work is the development of physics-based mathematical models of phage–bacteria interaction dynamics — how phages locate, bind to, infect, and replicate within bacterial targets at the population level — together with the large-scale numerical simulation of those models to identify and refine candidate therapies. To support that modeling, we run an in-house wet lab that generates the experimental data used to calibrate, test, and validate the underlying physical models. Two things make this a particularly interesting problem. First, we own our data generation loop. We are not just consuming static datasets and hoping they are good enough. We can generate new data, learn from it, and improve the system over time. Second, phages are one of the rare places in biology where the path from model output to human impact can be unusually short. We picked this area in part because, relative to most of biotech, the feedback loop to real-world use is fast. One way to describe what we are doing is simple: we are using computational and physics-based modeling to help design living therapies that can save lives. That sounds a little like science fiction, which is part of why we think it is worth doing. But it is also a very practical engineering and research problem. ## **The role** We are hiring a **Research Scientist** to help build the computational and physics-based modeling core of the company. This is a research-oriented role for a computational physicist (or someone with an equivalent quantitative, simulation-heavy background). We are looking for someone who is strong in numerical methods, large-scale simulation, and the modeling of complex dynamical systems, who likes difficult technical problems, and who wants to work on something where the connection between model quality and real-world impact is unusually direct. You will develop physics-based models of phage–bacteria infection dynamics, design and run large-scale numerical simulations, and validate those models against experimental data in close partnership with our wet lab. That collaboration is central to how we work. The lab exists in large part to generate and validate the data that improves our models. Over time, we expect that loop between model design, data generation, and biological validation to become one of the company's core advantages. We also picked this problem deliberately. A lot of scientifically important computational work sits very far from actual deployment in humans. In many cases, even very strong model progress may take years to affect a patient. Phages are different. They give us a much shorter path from model output to something that can matter in the clinic. In bio time, that is unusually fast. Because this is an early role, the job is not just to run experiments inside an existing system. The job is also to help define what the system should be. You will help shape how we think about research direction, model quality, experimentation standards, and the relationship between the computational and biological sides of the company. ## **What you'll do** - Develop physics-based theoretical and computational models of phage–bacteria infection dynamics, applying numerical simulation, statistical mechanics, and…