(Huber lab)[EMBL] welcomes applications for a PhD in ML for spatial omics [representation learning + integration with biostatistics, cell + anatomy foundation models, collaborate with domain scientists on cancer and dev:bio discovery science] |> Apply though the ELLIS portal ::: Deadline 2025-10-31

Happy to share that ShapeEmbed has been accepted at @neuripsconf.bsky.social 🎉 SE is self-supervised framework to encode 2D contours from microscopy & natural images into a latent representation invariant to translation, scaling, rotation, reflection & point indexing
📄 arxiv.org/pdf/2507.01009 (1/N)

EMBL International PhD Programme - winter recruitment 2026! 📣

Research groups across EMBL are recruiting now! www.embl.org/about/info/e...

Don’t miss this opportunity to receive dedicated mentoring while doing interdisciplinary research.

We are excited to share the publication of our paper on exploratory spatial statistics for spatial omics data

academic.oup.com/nar/article/...

Our paper benchmarking foundation models for perturbation effect prediction is finally published 🎉🥳🎉

www.nature.com/articles/s41...

We show that none of the available* models outperform simple linear baselines. Since the original preprint, we added more methods, metrics, and prettier figures!

🧵

Update: We greatly revised our paper and renamed it “Harnessing the Potential of Spatial Statistics for Spatial Omics Data with pasta”.

We discuss the broad range of exploratory spatial statistics options for spatial Omics technologies and show relevant use cases.

arxiv.org/abs/2412.01561

Very happy and proud to announce that the first preprint of my PhD is out: arxiv.org/abs/2504.20710

We developed an R package to translate mathematical models in SBML format into executable odin models and visualise models from @biomodels.bsky.social on our website Menelmacar biomodels.bacpop.org

#spatialproteomics #spatialbiology #multiplexedimaging #bioinformatics #python #scverse #opensource #singlecell #akoya #codex

It integrates with spatialdata and anndata, helping extend scverse toward spatial proteomics and imaging workflows.

We hope it helps researchers build flexible, powerful analysis pipelines for analyzing highly multiplexed fluorescence images.

🔧 Package: github.com/sagar87/spat...

𝐬𝐩𝐚𝐭𝐢𝐚𝐥𝐩𝐫𝐨𝐭𝐞𝐨𝐦𝐢𝐜𝐬 offers:
✅ A unified API for segmentation, image processing, cell phenotyping, and spatial statistics
✅Consistent handling of shared dimensions across data structures
✅Built on xarray and dask for high flexibility and memory efficiency
✅Easy installation and usage

Multiplexed imaging (CODEX, MICS, IMC) gives single-cell resolution at the protein level — but analyzing these datasets requires stitching together many different tools and data structures.

You need to manage images, masks, expression matrices, and keep them all consistent.

New preprint out!

We introduce 𝐬𝐩𝐚𝐭𝐢𝐚𝐥𝐩𝐫𝐨𝐭𝐞𝐨𝐦𝐢𝐜𝐬, a Python package for end-to-end processing and analysis of highly multiplexed immunofluorescence imaging data.

Built on xarray and dask, with seamless integration into the scverse ecosystem.
www.biorxiv.org/content/10.1...

Spatialproteomics - an interoperable toolbox for analyzing highly multiplexed fluorescence image data https://www.biorxiv.org/content/10.1101/2025.04.29.651202v1

Postdoc - Statistics and machine learning with spatial omics and single-cell perturbation data
www.huber.embl.de/group/posts/...

Mattermost is nice for communication, although it limits the file size, so maybe not ideal for sharing larger files. If you regularly share big files, maybe setting up ownCloud could be an option?

*Medical Data Scientist Postdoc* Program by Medical Faculty Uni Heidelberg. Join S.Dietrich, J.Lu & me to work on statistical& AI methods applied to single cell and spatial omics to improve immunotherapies:
www.medizinische-fakultaet-hd.uni-heidelberg.de/forschung/fo...
www.embl.org/about/info/m...