Want to work on Trustworthy AI? 🚀 I'm seeking exceptional candidates to apply for the Digital Futures Postdoctoral Fellowship to work with me on Uncertainty Quantification, Bayesian Deep Learning, and Reliability of ML Systems. The position will be co-advised by Hossein Azizpour or Henrik Boström.

👋Interested in Gaussian splatting and removing dynamic content from images? Our DeSplat is presented today at #CVPR2025 at Poster Session 1, ExHall D Poster #52. Yihao will be there to present our fully splatting-based method for separating static and dynamic stuff in images. 🧵[1/8]

You woke up early in the morning jet-lagged and having a hard time deciding for a workshop today @cvprconference.bsky.social ? Here's a reliable choice for you: our workshop on 🛟 Uncertainty Quantification for Computer Vision! 🗓️ Day: Wed, Jun 11 📍Room: 102 B #CVPR2025 #UNCV2025

KTH is looking for a *Postdoc* to work on visual domain adaptation for mobile robot perception in a joint project with Ericsson in Stockholm. Apply by May 15 if you are interested in working with computer vision applied to real robots! More info: www.kth.se/lediga-jobb/...

Submission deadline is extended to March 20 for submitting your paper to our #CVPR2025 workshop on Uncertainty Quantification for Computer Vision. Looking forward to see your submissions on recognizing failure scenarios and enabling robust vision systems! More info: uncertainty-cv.github.io/2025/

There is still time to submit your papers to our #CVPR2025 workshop on Uncertainty Quantification for Computer Vision, which is part of the workshop lineup at the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) in Nashville, Tennessee.

Our Workshop on Uncertainty Quantification for Computer Vision goes to @cvprconference.bsky.social this year! We have a super line-up of speakers and a call for papers. This is a chance for your paper to shine at #CVPR2025 ⏲️ Submission deadline: 14 March 💻 Page: uncertainty-cv.github.io/2025/

I will present ✌️ BDU workshop papers @ NeurIPS: one by Rui Li (looking for internships) and one by Anton Baumann. 🔗 to extended versions: 1. 🙋 "How can we make predictions in BDL efficiently?" 👉 arxiv.org/abs/2411.18425 2. 🙋 "How can we do prob. active learning in VLMs" 👉 arxiv.org/abs/2412.06014