Jason Hartford
@jasonhartford
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Dame Kathleen Ollerenshaw Fellow at @csmcr; Member of @ELLISforEurope; Research Unit Lead for the causality unit at @valence_ai. South African 🇿🇦
London, UK
Joined September 2009
We’re looking for a postdoc to work on Causality in Biological systems. You’ll be located at @GatsbyUCL and work with @ArthurGretton and me. Please reach out if you’re interested.
Research Fellow position open at @GatsbyUCL to work with me and @jasonhartford on Causality in Biological Systems! Apply at link, deadline is 27 August: https://t.co/QYkd4trSA6
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Research Fellow position open at @GatsbyUCL to work with me and @jasonhartford on Causality in Biological Systems! Apply at link, deadline is 27 August: https://t.co/QYkd4trSA6
ucl.ac.uk
UCL is consistently ranked as one of the top ten universities in the world (QS World University Rankings 2010-2022) and is No.2 in the UK for research power (Research Excellence Framework 2021).
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We’re looking for a postdoc to work on Causality in Biological systems. You’ll be located at @GatsbyUCL and work with @ArthurGretton and me. Please reach out if you’re interested.
Research Fellow position open at @GatsbyUCL to work with me and @jasonhartford on Causality in Biological Systems! Apply at link, deadline is 27 August: https://t.co/QYkd4trSA6
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John Kirby: The IDF is more careful with civilians than even the U.S. military! Green Beret sees the IDF in action, recoils in horror:
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🚀 Got fresh ideas in causal discovery, inference, or reasoning for scientific problems? Share them at the @CauScien workshop @ #NeurIPS2025! 📝✨ Submit your papers by 22 Aug 2025 →
sites.google.com
Call for Papers
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One more day to apply! Apply while you can:)
🚨 We’re hiring @sangerinstitute! Join us as a Postdoc or ML Scientist to build generative & foundation models for biology. 🧬 Spatial + single-cell omics 🧠 Diffusion, transformers, multimodal data 💊 Drug discovery via Open Targets Apply: Postdocs in computational biology
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Four more days to apply for postdoc and ML research scientist positions in my lab at @sangerinstitute at intersection of AIxBio
🚨 We’re hiring @sangerinstitute! Join us as a Postdoc or ML Scientist to build generative & foundation models for biology. 🧬 Spatial + single-cell omics 🧠 Diffusion, transformers, multimodal data 💊 Drug discovery via Open Targets Apply: Postdocs in computational biology
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Would love help identifying amazing ML researchers with strong connections to Canada who are currently outside Canada (thus potentially targets for recruitment as US situation deteriorates). DMs please. Retweet please.
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These (by @mic_caprio) are really a great set of slides--they do very well to motivate credal ML and more generally the basics of imprecise probabilities-based ML. They also provide a great set of foundational references (books + papers). https://t.co/6DRyfUjWEt
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Guess what??? Gen AI works for chemistry! 🤯💥 Generate co-folding → Generate ligand ensemble → Predict affinity → Generate synthesizable strong binders
Learn more about Boltz-2, the new open source AI model from MIT and Recursion capable of predicting protein binding affinity with unprecedented speed, scale and accuracy. ▪️ Boltz-2 is the first model to combine structure and binding affinity prediction, approaching the accuracy
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Fantastic work everyone
Excited to unveil Boltz-2, our new model capable not only of predicting structures but also binding affinities! Boltz-2 is the first AI model to approach the performance of FEP simulations while being more than 1000x faster! All open-sourced under MIT license! A thread… 🤗🚀
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📢 Machine Learning postdoc The EU 🇪🇺 Marie Sklodowska-Curie Actions (MSCA) Postdoctoral Fellowships scheme is your ticket to join the fun research at our Centre for AI Fundamentals in Manchester 🇬🇧 The 2025 call is open until Sep. 10 #AIFunManchester
https://t.co/sgNWuuVW22
marie-sklodowska-curie-actions.ec.europa.eu
The information provided on this page is a summary of the main rules and requirements for Postdoctoral Fellowships (PFs) and who can apply for them.
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1/ Introducing TxPert: a new model that predicts transcriptional responses across diverse biological contexts It’s designed to generalize across unseen single-gene perturbations, novel combinations of gene perturbations, and even new cell types 🧵
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What’s needed for a virtual cell to succeed in drug discovery? A new perspective paper from Recursion and our AI research engine @valence_ai lays out our vision for a virtual cell as a system that can reliably drive the discovery of new drugs via an iterative loop of: predict,
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There is something deeply ironic about Trump lecturing South Africa for not arresting a man for giving populist, racially charged speeches that target ethic minorities.
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For over a decade at @RecursionPharma, we've been driven by a core belief: truly understanding biology, and ultimately accelerating drug discovery, starts with building the right datasets. Every week we run millions of experiments in our highly automated wet lab to generate this
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Virtual Cells: Predict, Explain, Discover １．This paper outlines a bold vision for “virtual cells”—computational models that can predict cellular responses to perturbations, explain those responses through molecular mechanisms, and discover new biology via lab-in-the-loop
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Virtual cells shouldn’t just be predictive models, they should be testable theories of cell biology. They should predict cellular responses, explain them mechanistically, and discover new biology and therapies. Excited to share our perspective paper:
1/ At Valence Labs, @RecursionPharma's AI research engine, we’re focused on advancing drug discovery outcomes through cutting-edge computational methods Today, we're excited to share our vision for building virtual cells, guided by the predict-explain-discover framework 🧵
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I’m excited to have our perspective on virtual cells out.
1/ At Valence Labs, @RecursionPharma's AI research engine, we’re focused on advancing drug discovery outcomes through cutting-edge computational methods Today, we're excited to share our vision for building virtual cells, guided by the predict-explain-discover framework 🧵
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