Machine Learning in Science
We build probabilistic #MachineLearning and #AI Tools for scientific discovery, especially in Neuroscience. Probably not posted by @jakhmack.bsky.social.
📍 @ml4science.bsky.social, Tübingen, Germany
- Happy 2026 everyone! Two freshly minted PhDs 🧑🎓emerged from our lab at the end of last year. We congratulate Dr Julius Vetter (@vetterj.bsky.social) and Dr Guy Moss (@gmoss13.bsky.social)! Here seen celebrating with the lab 🎳. 1/3
- Our group is at NeurIPS and EurIPS this year with four papers and one workshop poster. If you are either curious about SBI with autoML, with foundation models, or on function spaces or about differentiable simulators with Jaxley, have a look below 👇 1/11
- Reposted by Machine Learning in Science[Not loaded yet]
- We are looking for a Research Engineer (E13 TV-L) to work at the intersection of #ML and #compneuro! 🤖🧠 Help us build large-scale bio-inspired neural networks, write high-quality research code, and contribute to open-source tools like jaxley, sbi, and flyvis 🪰. More info: www.mackelab.org/jobs/
- MackeLab has grown! 🎉 Warm welcome to 5(!) brilliant and fun new PhD students / research scientists who joined our lab in the past year — we can’t wait to do great science and already have good times together! 🤖🧠 Meet them in the thread 👇 1/7
- Simulation-based inference (SBI) has transformed parameter inference across a wide range of domains. To help practitioners get started and make the most of these methods, we joined forces with researchers from many institutions and wrote a practical guide to SBI. 📄 Paper: arxiv.org/abs/2508.12939
- This project brought together SBI researchers from 8 institutions across Europe—combining expertise from method developers and domain practitioners. Together, we aimed to consolidate years of experience into one comprehensive resource.
- Our work on training biophysical models with Jaxley is now out in @natmethods.nature.com. Led by @deismic.bsky.social, with @philipp.hertie.ai, @ppjgoncalves.bsky.social & @jakhmack.bsky.social et al. Paper: www.nature.com/articles/s41...
- This project was a collaborative effort by @deismic.bsky.social, @kyrakadhim.bsky.social, @matthijspals.bsky.social, @jnsbck.de, @hzwei.dev, Manuel Gloeckler, @lappalainenjk.bsky.social, @coschroeder.bsky.social, @philipp.hertie.ai, @ppjgoncalves.bsky.social & @jakhmack.bsky.social.
- Congrats to Dr Michael Deistler @deismic.bsky.social, who defended his PhD! Michael worked on "Machine Learning for Inference in Biophysical Neuroscience Simulations", focusing on simulation-based inference and differentiable simulation. We wish him all the best for the next chapter! 👏🎓
- The Macke lab is well-represented at the @bernsteinneuro.bsky.social conference in Frankfurt this year! We have lots of exciting new work to present with 7 posters (details👇) 1/9
