I am hiring a Research Scientist to work on a DARPA funded project in automated scientific feasibility assessment. In plain language, feasibility assessment tries to figure out either (1) whether a scientific claim that someone else made is likely to be true/feasible, or (2) given a scientific goal (like creating a specific technology), what is a viable/feasible pathway forward to creating that technology? The latter is the application that I’m particularly interested in, as it has the potential to speed the pace of scientific discovery.
There are many ways of attempting feasibility assessment, such as searching through the scientific literature, coming up with new experiments to run, and running those experiments to see if they suggest something is feasible or infeasible. A particular focus here is on automated experimentation — automatically generating, running, and analyzing code-based experiments. Currently it’s very easy to get a language model to generate code for experiments, but most of that experiment code is bad, incorrect, or otherwise has problems that make it untrustworthy. For example, the CodeScientist paper (linked below) found that only ~30% of the results of an automated experiment system turned out to be true, so some significant challenges in this subfield right now are figuring out how to design and run automated trustworthy experiments, and how to do all this at scale (one feasibility assessment might require dozens or more experiments). More broadly, feasibility assessment is a new and challenging task, and there are lots of exciting questions in how to frame these feasibility assessment problems and their component parts, evaluate them, make substantial progress, and release full systems that people can use with clear utility/impact.
Potentially relevant papers:
- Matter-of-Fact: A Benchmark for Verifying the Feasibility of Literature-Supported Claims in Materials Science (EMNLP 2025; https://aclanthology.org/2025.emnlp-main.203/ )
- CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation (ACL Findings 2025; https://aclanthology.org/2025.findings-acl.692/ )
The position details are below.
Application: https://arizona.csod.com/ux/ats/careersite/4/home/requisition/25414?c=arizona
Remote work: Possible
Start date: We’re hoping to find someone as soon as possible.
Application domains:
- Currently we’re working on claims in the AI domain.
- Previously (in Phase 1) we worked in the Materials Science Domain
- This might expand to the quantum computing domain in the future, but you do not need to have knowledge in that domain right now (we have some funding for domain expert assistance).