Anthropic opens a guest investigator programme for life-sciences labs

Saturday, 24 October 2026By Sridhar Mukkandi1 min read

Anthropic opened a call on 22 October for life-sciences researchers to work with its researchers on special projects. Principal investigators and their groups at academic institutions, nonprofits and government labs can apply for collaborations of 8 to 12 weeks, with Anthropic running its models and harnesses at no cost to them.

The focus areas are causal human biology, structural and computational molecular biology, medicinal chemistry, drug discovery and clinical science. Investigators keep the rights to their data and any follow-on research, and outputs are to be published with the data and code open-sourced. Applications are open for about 14 days from the announcement.

So what

If you work in a life-sciences lab, the window is short: about two weeks from 22 October. For everyone else, the published results and open code will be a useful look at how frontier models are being applied to real research problems.

SourceAnthropicwww.anthropic.com/news/open-call-guest-investigators-life-scienc Go deeper · NewsletterThe Sunday Letter

Drafted with AI from the original source, then checked and edited by Sridhar Mukkandi. Spotted a mistake? Write to hello@tarkika.com and we'll correct it here.

More from The Signal

Research Microsoft’s ThinkingBox turns database checks into rewards for training agentsChecking the state an agent leaves behind is a more honest test than reading its transcript, and the same check doubles as a training reward. Start with the three actions your agent takes most often. Microsoft and Hugging Face Agents Bayer Crop Science runs 22 read-only agents over its ML decision engine on AWSRead-only agents with narrowly scoped tools, launched to a small group first, is a pattern worth copying. So is testing a new model against your own tool calls before you switch: newer isn't always compatible. AWS Machine Learning Blog Tools AWS lets you write your own model-training loop on SageMaker without managing GPUsCustom reinforcement learning used to mean renting and babysitting GPUs. If you can write a check for a good outcome, you can now train on it without running the infrastructure yourself. AWS Machine Learning BlogAll stories