Our new preprint pushes causal machine learning into new scientific domains using probabilistic symmetry and geometry.

We are hiring at @jura.bsky.social both on the ML team (worldwide) and the wetlab team (Boston). Please get in touch if you've been thinking about it. ⏩ careers@jurabio.com

variational synthesis is now published, with the addition of large functional studies. we've now scaled the assays further, to train massive sequence-activity models www.jurabio.com/blog/scaling...

Scaling frontier models in vitro — JURA Bio, Inc.

We examine what becomes possible when AI controls the full loop of data generation and model training. Using MESA, our dataset of 20.9 billion antibody–pHLA interactions, we trained Vista transformer ...

jurabio.com

Nature Biotechnology@natbiotech.nature.com · 5mo ago

Manufacturing-aware generative models enable petascale synthesis of designed DNA - @lizbwood.bsky.social @jura.bsky.social go.nature.com/3NxXt1I

Ever wondered what it'd be like to run a de novo antibody campaign against 100 of the hardest targets, simultaneously, with 76% success rate, and have the complete wetlab validated results days from the project's start? We show you what that looks like, too: www.jurabio.com/mesa

Elizabeth Wood, PhD@lizbwood.bsky.social · 8mo ago

JURA is launching a new target intelligence dashboard called MESA jurabio.com/mesa with a nice interactive case study from our recent TCRm campaigns. Go check it out -- we'll open to crowd-sourced targets soon. @jura.bsky.social

Prediction is overhead when verification is cheap. We've spent years building a system where verification is cheap -- generationally so. This changes the logic of discovery in ways that are easy to underestimate. I tried to write a little about what that means: www.jurabio.com/blog/onebill...

One billion simultaneous experiments — JURA Bio, Inc.

For decades, drug discovery has been constrained by a simple fact: experiments are expensive, so you have to guess well. We built a system where you don't have to guess — testing a billion distinct m...

jurabio.com

We are at the #StartupVillage at #EurIPS today — come say hi 👋 @jura.bsky.social

Today we're releasing a technical blogpost on LIFT, a system that increases the information density of wetlab experiments by orders of magnitude without requiring more cells, more reagents, or more sequencing.

Applications are open for the @crg_eu PhD Programme! 20 fully funded positions — including one in our group through the Evolutionary Medical Genomics ITN. Join us to develop deep generative models of cross-species data to tackle open questions in disease genetics. www.crg.eu/en/content/t...

Centre de Regulació Genòmica (CRG)@crg.eu · 10mo ago

Are you looking for a PhD? Join us in Barcelona! You'll dive into a community of >100 PhD students from 30 countries exploring the frontiers of biology. You can also join an online workshop on 6 November (15:00 CET) to learn how to find the right lab for you. More info: www.crg.eu/en/content/t...

We're excited to present LeaVS, a method to scale up learning for protein function models. It is based on the co-design of wet lab experiments and in silico training.

For every Nobel that goes to a criminally under-recognized woman scientist (Brunkow, Karikó), or fails to go (Candy Lee), a week of mourning and reform for an academic system wherein you can do Nobel-prize-worthy-work and still end up without a conceivable path to being a professor.

Nature@nature.com · 10mo ago

BREAKING: The Nobel Prize in Physiology or Medicine has been awarded jointly to Mary E. Brunkow, Fred Ramsdell, and Shimon Sakaguchi "for their discoveries concerning peripheral immune tolerance" Stay tuned for more. #NobelPrize

You can read more in our post at www.jurabio.com/blog/leavs; preprint forthcoming. @jura.bsky.social @eliweinstein.bsky.social @mgollub.bsky.social @highvariance.bsky.social

LeaVS: Accelerating learning for biological AI — JURA Bio, Inc.

A fundamental lesson of modern AI is that scale is essential: training bigger models on bigger datasets unlocks new capabilities. A fundamental lesson of AI engineering is that scaling up isn't trivia...

jurabio.com

I'm looking for my first PhD student! We will push the frontiers of probabilistic machine learning for the molecular sciences, and study how to design new algorithms that exploit the unique properties of molecular systems to learn about the world. efzu.fa.em2.oraclecloud.com/hcmUI/Candid...

PhD scholarship in Machine Learning for Molecules - DTU Chemistry

Advance large scale data generation for chemical and biological AI in a 3 year PhD. Work on the frontier of active learning, and develop novel probabilistic machine learning techniques for experiment ...

efzu.fa.em2.oraclecloud.com

So excited about this - we did iterative generative design at large scale with variational synthesis, and got human scFv candidates against some of the hardest therapeutic targets around.

Elizabeth Wood, PhD@lizbwood.bsky.social · last yr.

The biggest challenge for AI in biology isn't just models, it's the data used to train them. Standard biological data isn't built for AI. To unlock generative AI for drug discovery, we must rethink how we generate and capture data. 1/

🔊 The call for the first round of open PhD fellowships from the newly formed Danish Advanced Research Academy (DARA) has just been announced: daracademy.dk/fellowship/f... Exceptional candidates with a strong background in theoretical chemistry are more than welcome to reach out to me for support.

We can make population genetics studies more powerful by building priors of variant effect size from features like binding. But we’ve been stuck on linear models! We introduce DeepWAS to learn deep priors on millions of variants! #ICML2025 Andres Potapczynski, @andrewgwils.bsky.social 1/7

Thrilled to announce that I am joining DTU in Copenhagen in the fall, as an assistant professor of chemistry. My research group will focus on fundamental methodology in machine learning for molecules.

The BayesComp workshop on 'Bayesian Computation and Inference with Misspecified Models' will take place in Singapore on the 16-17th June. We have an open call for posters/contributed calls, with a deadline on the 1st May. More details on the website: postbayes.github.io/BayesMisspec...

This Thursday, 1PM, @eweinstein.bsky.social is headed back to CMU, this time to the Machine Learning Department. He'll be sharing his work on hierarchical causal models. If you're on campus, I encourage to you check it out! www.ml.cmu.edu/calendar/

Calendar - Machine Learning - CMU - Carnegie Mellon University

Calendar of the Machine Learning Department at Carnegie Mellon University

ml.cmu.edu

Tomorrow -- another chance to learn about variational synthesis, probabilistic experimental design methods, & ways to capture scaled functional data for model training: this time with zoom access for those of you remote, 1pm at @cmu.edu Computer Science. Details at: www.cs.cmu.edu/calendar/181...

Today at UPenn's CIS Seminar: a chance to hear about variational synthesis, probabilistic experimental design methods, and ways to capture modern ML-scale functional data for biology: events.seas.upenn.edu/event/13596/

1/n I'm thrilled to share *Deep generative modelling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders* t.co/DoN4DrSFbS from a wonderful collaboration between the Marks Lab and Dias and Frazer group, with @roseorenbuch as lead! 🧵:

The news came out! Last week we signed a deal with the fantastic team at Replay Bio and their product company Syena to develop a KRAS G12D specific therapeutic TCR therapy. So proud and excited! www.businesswire.com/news/home/20...