Research Scientist Jobs

About The Position

Requirements

• A Ph.D. in Physics or a closely related quantitative field (applied mathematics, computational science, or similar), or equivalent research experience.
• Strong background in numerical methods, large-scale simulation, and the modeling of complex dynamical systems, including systems governed by partial differential equations.
• Depth in one or more of: statistical mechanics, optimization theory, probabilistic and statistical methods, and numerical stability and error analysis.
• Experience with large-scale scientific computing and high-performance computing environments.
• Fluency in Python and the modern scientific-computing stack.
• Comfort working in an early-stage environment where the problems are hard and the path is still being defined.
• Ability to work closely with domain experts outside your own field.

Nice To Haves

• Familiarity with the modern machine learning stack (e.g., PyTorch) is a plus.
• Substantial modeling and simulation work, not just building products on top of model APIs.
• Enjoy designing experiments and learning from ambiguous results.
• Excited by the idea of applying physics and computation to something more concrete than the usual software treadmill.
• Want work to be tied to outcomes in the real world.
• Interested in helping build a company where computational and physics-based research is central, not decorative.

Responsibilities

• Develop physics-based theoretical and computational models of phage–bacteria infection dynamics, applying numerical simulation, statistical mechanics, and optimization theory.
• Investigate the underlying biophysics of phage infection — binding kinetics, replication dynamics, and population-level interactions — and translate these physical phenomena into tractable mathematical and computational form.
• Design and implement numerical methods and software for large-scale simulation and analysis of these physical systems.
• Perform mathematical analysis of the underlying models, including numerical stability analysis, error analysis, and validation against experimental data.
• Analyze experimental data using mathematical and statistical methods to infer physical parameters and constrain the underlying models.
• Design and conduct computational experiments, in close coordination with our wet-lab scientists, to test and refine the models.
• Help define the research roadmap for how Tabula's models should improve over time, and build better practices around experimentation, evaluation, and reproducibility.
• Help a small team build an AI-first company whose output is not content or software alone, but real therapies for real patients.

Benefits

• AI-first therapeutics company
• Addressing antibiotic resistance
• Physics-based mathematical modeling
• Large-scale numerical simulation
• In-house wet lab for data generation
• Shorter path from research to real-world impact
• Challenging technical problems
• Direct real-world impact
• Room to shape work and direction
• Opportunity to build a company from the beginning