PhD Research Fellow in Trustworthy Machine Learning (296254) | University of Oslo Job title: PhD Research Fellow in Trustworthy Machine Learning (296254), Employer: University of Oslo, Deadline: Tuesday, March 24, 2026

We are hiring a PhD student at the SCML group, University of Oslo! 🎓

When can clustering be trusted? This project will use simulations to probe the robustness of unsupervised ML, with applications in the life sciences.

🗓️Deadline: 24 March
📝To apply: www.jobbnorge.no/en/available...

📢 We are announcing the Adaptive Immune Profiling Challenge 2025!
Can you predict immune state labels from adaptive immune receptor repertoires?
💰 $10,000 prize pool!
🗓️ Launches Nov 5 on @kaggle.com
More Info: uio-bmi.github.io/adaptive_imm...

PhD Fellowship in Computational Systems Immunology (286608) | University of Oslo Job title: PhD Fellowship in Computational Systems Immunology (286608), Employer: University of Oslo, Deadline: Wednesday, October 15, 2025

We’re recruiting a PhD Fellow in Computational Systems Immunology. Work on large-scale immune receptor datasets and develop computational models in close collaboration with experimental labs. Apply here: www.jobbnorge.no/en/available...

Happy to share our new collaborative work! 🚨 We analyzed 2250 TCR repertoires to uncover how HLA risk alleles shape immune autoreactivity in T1D. T1D-specific HLA-motifs were also validated in pancreatic lymph nodes (pLN) & spleen. 🧬🧵 #T1D #Immunology #TCR 1/9

Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases The circulating antibody repertoire is crucial for immune protection, holding significant immunological and biotechnological value. While bottom-up mass spectrometry (MS) is the most widely used prote...

We created one of the largest antibody bottom-up proteomics datasets to query the impact of experimental and computational parameters on the reconstruction of antibody sequences. www.biorxiv.org/content/10.1... Brilliant work by @mchernigovskaya.bsky.social, Khang Le Quy and Igor Snapkow.

Improving generalization of machine learning-identified biomarkers using causal modelling with examp... Machine learning is increasingly applied for disease diagnostics due to its ability to discover differentiating features in data. However, the clinical applicability of these models remains a challeng...

Our perspective on the generalization of high-dimensional biomarkers identified by machine learning where we argue for considering causal models was just published at t.co/pLhcgkOTy1

🧬 Opening for a postdoc in our vibrant team at NCMM, University of Oslo, to generate deep learning models to enhance JASPAR & UniBind, boosting gene regulation research. The project is in collab with @anshulkundaje.bsky.social and @wywywa.bsky.social. Plz share.

www.jobbnorge.no/en/available...

🎉 Happy to share my new preprint in which we present LIgO — a powerful tool to simulate adaptive immune receptor (AIR) and repertoire (AIRR) data for the development and benchmarking of AIRR-based ML

www.biorxiv.org/content/10.1...

Try LIgO now! 🚀
github.com/uio-bmi/ligo